Eastern Research Grou p, Inc. 

# 2005 MEXICALI EMISSIONS INVENTORY

Final Report

Prepared for:
Western Governors’ Association Denver, Colorado U.S. Environmental Protec tion Agency, Region 9 San Francisco, California Secretaría de Protección al Ambiente de Baja California Mexicali, Baja California, Mexico



## February 27, 2009

### ERG Project No: 3657.00.004.001


# 2005 MEXICALI EMISSIONS INVENTORY

Final Report

Prepared for: Western Governors’ Association Denver, Colorado U.S. Environmental Prot ection Agency, Region 9 San Francisco, California Secretaría de Protección al Ambiente de Baja California Mexicali, Baja California, Mexico

Prepared by: Eastern Research Group, Inc. (ERG) Sacramento, California with Ingeniería en Control Ambiental y Ries go Industrial, S. de R.L. (ICAR) Mexicali, Baja California, Mexico and TransEngineering

El Paso, Texas
February 27, 2009

# TABLE OF CONTENTS
Section Page
ES.0 EXECUTIVE SUMMARY ...........................................................................................ES-1 ES.1 Methodology..................................................................................................... ES-1 ES.2 Summary of Results.......................................................................................... ES-2
1.0 INTRODUCTION ........................................................................................................... 1-1 1.1 Inventory Characteristics ..................................................................................... 1-2 1.2 Data Management and Formatting....................................................................... 1-5 1.3 Data Quality Objectives....................................................................................... 1-5 1.4 Report Organization............................................................................................. 1-7
2.0 POINT SOURCES........................................................................................................... 2-1 2.1 Federal Jurisdiction Point Source Methodology.................................................. 2-1 2.2 State Jurisdiction Poin t Source Methodology......................................................2-4 2.3 Results.................................................................................................................. 2- 7 2.4 Quality Assurance................................................................................................ 2-8
3.0 AREA SOURCES............................................................................................................ 3-1 3.1 Methodology........................................................................................................ 3-1 3.1.1 Fuel Combustion Categories.................................................................... 3-5 3.1.2 Evaporative VOC Categories................................................................... 3-5 3.1.3 Agricultural Categories............................................................................ 3-6 3.1.4 Ammonia Categories ...............................................................................3-8 3.1.5 Paved Road Dust...................................................................................... 3-8 3.1.6 Unpaved Road Dust ............................................................................... 3-12 3.1.7 Miscellaneous Burning Categories ........................................................ 3-13 3.1.8 Border Crossings.................................................................................... 3-13 3.1.9 Brick Kilns............................................................................................. 3-13 3.1.10 Charbroiling, Construction Dust, and Bakeries .................................... 3-14 3.1.11 Landfills ................................................................................................ 3-14 3.1.12 Wastewater Treatment .......................................................................... 3-14 3.2 Results3-14 3.3 Quality Assurance.............................................................................................. 3-19
4.0 ON-ROAD MOBILE SOURCES.................................................................................... 4-1 4.1 Methodology........................................................................................................ 4-1 4.2 Results4-2 4.3 Quality Assurance................................................................................................ 4-4
5.0 NONROAD MOBILE SOURCES .................................................................................. 5-1 5.1 Methodology........................................................................................................ 5-1

2005 Mexicali Emissions Inventory 

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# TABLE OF CONTENTS (Continued)
Section Page
5.1.1 Aircraft..................................................................................................... 5-1 5.1.2 Locomotives............................................................................................. 5-2 5.1.3 Agricultural and Construction Equipment............................................... 5-2 5.2 Results..................................................................................................................5- 3 5.3 Quality Assurance................................................................................................ 5-4
6.0 EMISSION INVENTOR Y RESULTS............................................................................ 6-1
7.0 REFERENCES ................................................................................................................ 7 -1

APPENDIX A: AREA SOURCE CATEGORY FORMS APPENDIX B: TECHNICAL REPORT (D RAFT), ESTIMATION OF TRAVEL DEMAND AND VEHICULAR TRAFFIC CONGESTION OF THE MEXICALI REGION Tables Page
ES-1 2005 Mexicali Emissions Inventor y Summary (Tons per Year) ...................................ES-3 1-1 2005 Mexicali Emissions Inventor y Summary (Tons per Year) ..................................... 1-5 2-1 Listing of State Jurisdiction Point Sources ...................................................................... 2-5 2-2 2005 Mexicali Federal Jurisdiction Point Source Emissions (Tons per Year) ................ 2-9 2-3 2005 Mexicali State Jurisdiction Point Source Emissions (Tons per Year) .................... 2-9 3-1 General Emission Estimation Methodologi es for Area Sources in Mexicali .................. 3-2 3-2 2005 Mexicali Area Source Emi ssions (Tons per Year) ............................................... 3-15 4-1 2005 Mexicali On-Road Mobile Sour ce Emissions (Tons per Year).............................. 4-4 4-2 2005 Imperial County and Mexicali On-Road Mobile Source Emissions (Tons per Year)............................................................................................................... . 4-6 5-1 2005 Mexicali Nonroad Mobile Source Emissions (Tons per Year)............................... 5-4 6-1 2005 Mexicali Emissions Inventor y Summary (Tons per Year) ..................................... 6-1

2005 Mexicali Emissions Inventory 

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### Figures Page
1-1 State of Baja California and Five Municipalities............................................................. 1-3 1-2 Municipality and Mexicali a nd Most Populous Localities .............................................. 1-4 2-1 Location of Federal and State Jurisdiction Point Sour ces in the Urban Portion of Mexicali.................................................................................................................... ... 2-3 3-1 Location of Paved and Unpaved Road Dust Sampling Sites........................................... 3-9 3-2 Pavement Coverage of Mexicali Roads......................................................................... 3-11 4-1 Mexicali Loaded Roadway Netw ork Under Weekday Conditions.................................. 4-3 6-1 Mexicali NO x 

### Emissions.................................................................................................. 6-2 6-2 Mexicali SO 2 

### Emissions .................................................................................................. 6-2 6-3 Mexicali VOC Emissions ................................................................................................ 6-3 6-4 Mexicali CO Emissions ................................................................................................... 6-3 6-5 Mexicali PM 10 

### Emissions ................................................................................................ 6-4 6-6 Mexicali PM 2.5 

### Emissions................................................................................................ 6-4 6-7 Mexicali NH 3 

### Emissions.................................................................................................. 6-5 6-8 Mexicali CH 4 

### Emissions.................................................................................................. 6-5

2005 Mexicali Emissions Inventory 

### iii Final, 2/27/09

# ACRONYMS
AGEB area geoestadístics bási cas (basic geographic area) ANAFAPYT Asociación Nacional de Fabricantes de Pinturas y Tintas (National Association of Paint and Dye Manufacturers) ARB Air Resources Board, California BC Baja California CANALAVA Cámara Nacional de la Industria de Lavanderías (Nationa l Chamber of the Dry Cleaning Industry) CFE Comisión Federal de Electricida d (Federal Electric Commission) CH 4 

### methane CO carbon monoxide COA Cédula de Operación Anual (Annual Operating Report) DQOs data quality objectives
EIIP Emission Inventory Improvement Program
EIP Emission Inventory Plan ERG Eastern Research Group, Inc. FIFRA Federal Insecticide, Fungicide and Rodenticide Act GIS geographical information system
HDDV heavy-duty diesel vehicle HDGV heavy-duty gasoline vehicle IC internal combustion ICAO International Civ il Aviation Organization ICAR Ingeniería en Control Ambienta l y Riesgo Industrial, S. de R.L.
ID identification number INEGI Instituto Nacional de Es tadística, Geografía e Informática (National Institute of Statistics, Geography, and Computing) kg kilogram

2005 Mexicali Emissions Inventory 

### iv Final, 2/27/09

### km kilometers lb pound lbs pounds LDDT light-duty diesel truck LDDV light-duty diesel vehicle LDGT light-duty gasoline truck LDGV light-duty gasoline vehicle LPG liquefied petroleum gas LTO landing and take-off

m 2 

### square meters

m 3 

### cubic meters MC motorcycle Mg megagram mm Hg millimeters of mercury mph miles per hour NAICS North America Industr y Classification System NEI National Emissions Inventory
NH 3 

### ammonia
NIF National Emissions I nventory Input Format NO x 

### nitrogen oxides NPK nitrogen-phosphorus-potassium PEMEX Petróleos Mexicanos PM particulate matter PM 2.5 

### particulate matter less than 2.5 micr ometers in equivalent aerodynamic diameter.
PM 10 

### particulate matter less than 10 micromet ers in equivalent aerodynamic diameter

2005 Mexicali Emissions Inventory 

### v Final, 2/27/09

### PROAIRE Programa para Mejorar la Calidad de l Aire (Program to Improve Air Quality) QA quality assurance QAPP quality assurance project plan QC quality control s second SAGARPA Secretaría de Agricultura, Ganade ría, Desarrollo Rural, Pesca y Alimentación (Secretariat of Agriculture, Livestock, Rural Development, Fisheries, and Food) SCC source classification code scf standard cubic feet SEMARNAT Secretaría de Medio Ambiente y Recursos Naturales (Secretariat of the Environment and Natural Resources) SENER Secretaría de Energí a (Secretariat of Energy) SLRC San Luis Río Colorado, Sonora SO 2 

### sulfur dioxide SPA Secretaría de Protección al Ambien te de Baja Califor nia (Secretary of Environmental Protection of Baja California) TDM travel demand model TIM time-in-mode TSP total suspended particulate U.S. EPA United States Environmental Protection Agency VKT vehicle kilometers traveled VMT vehicle miles traveled VOC volatile organic compound WGA Western Governors’ Association WRAP Western Regional Air Partnership
yr year

2005 Mexicali Emissions Inventory 

### vi Final, 2/27/09

# ES.0 EXECUTIVE SUMMARY
In response to requests from the Secretary of Environmental Protecti on of Baja California (Secretaría de Protección al Ambiente de Baja California – SPA) and the California Air Resources Board (ARB), the Western Governors’ Association (WGA), w ith funding from the U.S. Environmental Protection Agency (U.S. EPA) , hired a contractor team to develop the 2005 Mexicali inventory. The contractor team was led by Eastern Research Group, Inc. (ERG) and supported by two Mexico-based subcontractors (i .e., Ingeniería en Control Ambiental y Riesgo Industrial, S. de R.L. [ICAR] and TransEngin eering). This 2005 Mexi cali emissions inventory updates previous inventories developed for Mexica li, Baja California (BC) , including Mexicali’s Programa para Mejorar la Calidad del Aire (PROAIRE) for a base year of 1996 (PROAIRE, 1999) and the 1999 Mexico National Emissions Inventory (NEI) Program (ERG, 2006). The 2005 Mexicali emissions inventory was deve loped for the entire municipality of Mexicali. Annual emissions (both tons per year and megagrams per year) were estimated for point sources, area sources, on-road mobile s ources, and nonroad mobile sources. Emissions were estimated for the following pollutants: nitrogen oxides (NO x 

### ), sulfur dioxide (SO 2 

### ), volatile organic compounds (VOC), carbon monoxide (CO), particul ate matter (PM) less than 10 micrometers (μm) in aerodynamic diameter (PM 10 

### ), PM less than 2.5 μm in aerodynamic diameter (PM 2.5 

### ), ammonia (NH 3 

### ), and methane (CH 4 

### ). ES.1 Methodology The point source emissions inventory was developed using existing emissions data provided by the Secretariat of Environment and Natural Resources (SEMARNAT) and the Secretary of Environmental Prot ection of Baja Califor nia (SPA). The area source and nonroad mobile source emissions inventory were developed using activity data co llected from Mexicali government agencies and other local contacts. The on-road mobile source emission inventory was developed using vehicle activit y data (i.e., vehicle kilometers traveled [VKT]) obtained from traffic and congestion modeling.

2005 Mexicali Emissions Inventory 

### ES-1 Final, 2/27/09

### ES.2 Summary of Results Table ES-1 provides a summar y of the results of the 2005 Mexicali emissions inventory, by source type and pollutant. Table ES-1 shows that:

• Emissions from federal jurisdiction point sources are considerably larger than emissions from state jurisdiction point s ources, even though the ratio of state-tofederal jurisdiction point sour ces is greater than 10-to-1.

• NO x 

### and SO 2 

### emissions are dominated by point sources.

• VOC emissions are emitted by area (i.e., mainly various evaporative source categories) and on-road mobile sources.

• On-road mobile sources are the predominant source of CO.

• Area sources are the largest contributors of PM 10 

### and PM 2.5 

### (i.e., due to unpaved and paved road dust), NH 3 

### (i.e., from livestock ammonia and fertilizer application), and CH 4 

### (i.e., from the landfill). The final inventory was formatted to be comp atible with U.S. EPA’s National Emissions Inventory Format (NIF), and SMOKE/IDA form at (i.e., used for photochemical and other modeling). In addition, spatial surrogates for gridding area sour ce emissions into a 4 km grid system for modeling were developed. Also, ge ographic information system (GIS)-based shape files obtained for Mexicali’s AGEBs (i.e., area geoe stadístics básicas or basic geographic areas) which were used in the development of some area source category emissions, were provided along with the final inventory files for purposes of spatially distributi ng area source emissions over the inventory domain for pur poses of air quality modeling.

2005 Mexicali Emissions Inventory 

### ES-2 Final, 2/27/09

### Table ES-1. 2005 Mexicali Emissions Inventory Summary (Tons per Year) Source Type NO x 

### SO 2 

### VOC CO PM 10 

### PM 2.5 

### NH 3 

### CH 4 

### Point – Federal 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 Point – State 444.0 973.2 73.0 479.9 89.7 Area 1,205.5 131.1 15,301.5 18,854.0 50,879.3 6,753.5 9,015.2 6,033.3 On-Road Mobile 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 Nonroad Mobile 4,485.0 60.9 542.9 3,206.9 550.2 533.3 0.0 1.5 Total 28,636.5 4,984.8 25,553.8 86,780.4 53,821.8 8,081.0 9,267.6 6,179.2 Blanks indicate that pollutant emissions were not applicable or were not estimated due to a lack of data.

2005 Mexicali Emissions Inventory 

### ES-3 Final, 2/27/09

# 1.0 INTRODUCTION
The first emissions inventory developed for Mexicali, Baja California (BC) was published as part of Mexicali’s Programa para Mejorar la Calidad del Aire (PROAIRE) for a base year of 1996 (PROAIRE, 1999). The next and most recent emissions inventory for Mexicali was developed and published as part of the 1999 Mexico National Emissions Inventory (NEI) Program; however, the industrial source em issions in the 1999 Mexico NEI for Mexicali were based on the previous 1996 PR OAIRE inventory (ERG, 2006). The Secretary of Environmental Protection of Ba ja California (Secretaría de Protección al Ambiente de Baja California – SPA) has requested an update to the emissions inventory for the municipality of Mexicali for the year 2005. In addition, the California Air Resources Board (ARB) has requested an updated in ventory for purposes of air quality modeling in the border area of Imperial County, Calif ornia, and Mexicali. In response to these needs, the Western Governors’ Associati on (WGA), with funding from the U.S. Environmental Protection Agency (U .S. EPA), sponsored this project to update the Mexicali emissions inventory to 2005. The C ontractor team, hired under Contract No. 30430-01, was led by Eastern Research Group, Inc. (ERG), a nd included Ingeniería en Control Ambiental y Riesgo Industrial, S. de R.L. (ICAR), and Tran sEngineering. ICAR was responsible for data collection related to ar ea and point sources, and TransEngine ering was responsible for estimating on-road motor vehicle activity data which were us ed to estimate mobile source emissions and to determine the distribution of vehicle activity over pa ved versus unpaved roads. The specific objectives of the 2005 Mexicali emissions inventory were to provide a current and accurate technical ba sis for air quality planning in th e municipality of Mexicali. Also, the results of the 2005 Mexicali emissions inventory (for the non-methane pollutants) are formatted for use in air quality modeling to better understand the c ontribution of Mexicali emissions to concentrations of crite ria pollutant levels in that region.

2005 Mexicali Emissions Inventory 

### 1-1 Final, 2/27/09

### 1.1 Inventory Characteristics The characteristics of the 2005 Mexicali emissions inventory are as follows:

• Base year: 2005.

• Temporal resolution: Annual emissions, in terms of megagrams per year (Mg/year) and tons per year (tons/year). Some inte rmediate activity data are presented in Imperial units. The inventory results are pr esented in Imperial units (i.e., tons per year) in the English version of the final report and in metric units (i.e., megagrams [Mg] per year) in the Spanish version of the final report.

• Geographic domain: Entire municipality of Mexicali. Figure 1-1 shows the entire state of Baja California and its five muni cipalities (i.e., Mexicali, Tijuana, Tecate, Playas de Rosarito, and Ensenada). Figur e 1-2 shows the munici pality of Mexicali and location of the most populous “localities” (e.g., cities , towns, communities, etc.) with population greater than 3,000.

• Geographic Resolution: In addition to tota l municipality-level emissions by pollutant and source type, emissions were estimated at various other levels of detail, including:

− Exact location (i.e., latitude /longitude) for point sources.

− Spatial surrogates at the level of basi c geographic areas (area geoestadística básica – AGEB) are also provided. An AGE B is analogous to a census tract in the U.S. These surrogates provide the spatial allocation of emissions to 4 kilometer (km) × 4 km grid cells w ithin the ARB modeling domain (i.e., approximately 36 km to 50 km south of the California—BC border).

• Pollutants:

− Nitrogen oxides (NO x 

### )

− Sulfur dioxide (SO 2 

### )

− Volatile organic compounds (VOC)

− Carbon monoxide (CO)

− Particulate matter (PM) le ss than 10 micrometers (μm) in aerodynamic diameter (PM 10 

### )

− PM less than 2.5 μm in aerodynamic diameter (PM 2.5 

### )

− Ammonia (NH 3 

### )

− Methane (CH 4 

### ).

• Source Types and Categories: Point sources , area sources, on-road mobile sources, and nonroad mobile sources. Each of these source types will be discussed in their individual sections.

2005 Mexicali Emissions Inventory 

### 1-2 Final, 2/27/09

### Figure 1-1. State of Baja Ca lifornia and Five Municipalities

2005 Mexicali Emissions Inventory 

### 1-3 Final, 2/27/09

### Figure 1-2. Municipality of Mexica li and its Most Populous Localities

2005 Mexicali Emissions Inventory 

### 1-4 Final, 2/27/09

### 1.2 Data Management and Formatting Development of the 2005 Mexicali emissions inventory required the compilation of considerable amounts of emissions and other re lated inventory data. Electronic spreadsheets were used to compile, calculate, and summarize emissions data. The final inventory was formatted to be comp atible with U.S. EPA’s National Emissions Inventory Format (NIF), and SMOKE/IDA form at (i.e., used for photochemical and other modeling). In addition, spatial su rrogates for gridding the area source emissions into a 4 km grid system for modeling will be developed. Also, ge ographic information syst em (GIS)-based shape files of the obtained for Mexicali’s AGEBs, whic h were used in the development of some area source category emissions, will be provided along with the final inventory files for purposes of spatially distributing area source emissions over the inventory domain for purposes of air quality modeling.
1.3 Data Quality Objectives
The data quality objectives (DQOs) were esta blished as part of the Emission Inventory Plan (EIP)/Quality Assurance Project Plan (QA PP) developed for this project. The specific DQOs, and criteria used to evaluate whet her the DQOs are achieved for the 2005 Mexicali emissions inventory are as follows:

1. A comprehensive emissions inventory for all source types (i.e., point, area, on-road motor vehicle, and nonroad mobile sources ) and all major categories within these source types. Acceptance criteria: The source types and major categories accounted for in the EIIP guidance documents are present in th e Mexicali emissions inventory, and/or any differences are documented. 2. Identification of 80% of a ll federal jurisdiction and stat e jurisdiction point sources located within the municipality of Mexicali. Acceptance criteria: A list of poin t sources under the federal (SEMARNAT Delegation), and state (SPA) jurisdictions co mpares to the emissions data, by source, provided by those agencies. 3. Point sources: Emissions estimates with an accuracy of ± 25% for at least 10% of the facilities.

2005 Mexicali Emissions Inventory 

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### Acceptance criteria: Estimated emissions fo r 10% of the total facilities compare to measured emissions of all pollutants w ith ± 25% accuracy. The emissions data measurements will be obtained from subcontractor ICAR, and will be based on data collected over the past five years in Mexicali. 4. Area, On-Road Motor Vehicle, and Nonroa d Mobile Sources: Emissions estimates with an accuracy of ±50%. Acceptance criteria: Estimated emissions using emission factors/models compare to emissions estimated using a fuel-balance approach for all applicable pollutants. The fuel-balance approach will use national statistics, while the preferred inventory methodology will use PEMEX and local fuel distribution data. 5. Documentation: All EI results are transparent and reproducible. Acceptance criteria: Written and electr onic inventory documentation provides detailed explanations of data collect ed, data measured/analyzed, values estimated/calculated, assumptions made, and em ission factors used so that the results can be duplicated. Most of these acceptance criteria have been met, and in some cases exceeded, in the development of the 2005 Mexicali emissions invent ory. Instances where the acceptance criteria could not be met are documented (i.e ., see Area Sources, Section 3.3). In general, the following quality assurance checks were performed to ensure the DQOs were achieved:

• Accuracy checks – Spreadsheets were devel oped to calculate emissions, and 100% of the formulas were checked for accuracy. 100% of the electronic calculations were checked (50% by hand), and a ny errors were corrected.

• Completeness checks – The list of pollu tants and source categories for the 1999 Mexico NEI was compared against all of the inventory files to en sure that emissions are estimated for the correct pollutants and source categories. Any errors were corrected.

• Representativeness – The emission factors and activity data were reviewed to ensure they are appropriate and current for th e source categories for 100% of the source categories inventories. All ad justment factors (e.g., used to allocate national activity to a state- or municipality-level rate) were reviewed to ensure they are representative of actual conditions affecting emissions generated during the base year of 2005. Specific quality assurance procedures followed during the estimation of emissions for each source type will be described in report Sections 2.0, 3.0, 4.0, and 5.0 for point sources, area

2005 Mexicali Emissions Inventory 

### 1-6 Final, 2/27/09

### sources, on-road mobile sources, and nonroad mobile sources, respectively, as part of the final report.
1.4 Report Organization The remainder of this report provides a comp lete and detailed description of all methods, assumptions, data, and calculations used to develop the emissions inventory, along with the results by source category. The report is organized as follows:

• Section 2.0 describes the point source estimation methodology and results.

• Section 3.0 describes the area source estimation methodology and results.

• Section 4.0 describes the on-road mobile source estimation methodology and results.

• Section 5.0 describes the nonroad mob ile source methodology and results.

• Section 6.0 provides the overall 2005 Me xicali emissions inventory results.

• Section 7.0 lists all references used in th e development of this emissions inventory.

• Appendix A contains the forms showing how each area source category’s emissions were calculated (i.e., method/emission factor/f ormula, reference, activity data, sample calculation).

• Appendix B contains a detailed explanati on of the analysis conducted to estimate onroad mobile source activity data.

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# 2.0 POINT SOURCES
Point sources are stationary industrial source s that release emissions from fixed points (e.g., a stack or vent). In Mexi co, point source emissions data are transmitted to the federal and/or state environmental agen cies (depending upon sector-based jurisdiction) by means of the Annual Operating Certificate (Cédula de Op eración Annual – COA). As a result, the development of point source emissions data for the 2005 Mexicali emissions inventory relied heavily on the cooperation of the government agenci es that maintain emission registries – the Secretariat of Environment and Natural Resour ces (SEMARNAT) for federal jurisdiction point sources and the SPA for state jurisdiction point sources. The procedures used to compile emissions from the federal and state juri sdiction point sources are described below.
2.1 Federal Jurisdiction Point Source Methodology There are 16 federal jurisdiction point sources located in the munici pality of Mexicali, which was confirmed by SEMARNAT. These poi nt sources are listed below with their sector/subsector, as applicable:

• CFE Campo y Centrales Geoter moeléctrico de Cerro Prieto (Electricity Generation)

• CFE Central Turbo Gas (El ectricity Generation)

• Dafmex, S. de R.L. de C.V. (Met allurgical/F errous Metal Foundry)

• Energía Azteca X, S. de R.L. de C.V. (Electricity Generation)

• Energía de Baja California, S. de R.L. de C.V., Central Termoe léctrica (Electricity Generation)

• Fábrica de Papel San Francisco, S.A. de C.V. (Pulp and Paper/Paper Production)

• Fevisa Industrial, S.A. de C.V. (Glass/Container Gl ass Manufacture)

• Fundiciones San Martin o Felix Jesus del Va lle Zamora (Metallurgical/Iron and Steel Foundry)

• Infra, S.A. de C.V., Sucursal Me xicali (Chemical/Industrial Gases)

• Kenworth Mexicana, S.A. de C.V. (Automotive/Truck Manufacture)

• Kyowa Mexicali, S.A. de C.V. (Plastic Parts Manufacturing)

2005 Mexicali Emissions Inventory 

### 2-1 Final, 2/27/09

### • Maquiladora San Diego, S.A. de C.V. (Met allurgical – Iron an d Steel Foundry)

• Petróleos Mexicanos (PEMEX) Refinación Planta Mexicali (Petroleum/Petroleum Products Storage and Distribution

• Quimical, S.A. de C.V. (Chemical/Pes ticides and Agricultural Chemicals)

• Termoeléctrica de Mexicali, S. de R.L. de C.V. (Electricity Generation)

• Vitro AFG, S.A. de C.V. (Glass/Plane Glass Manufacture) The physical location of some of the federa l jurisdiction (and stat e jurisdiction) point sources in the urban portion of Me xicali is illustrated in Figure 2-1. The federal jurisdiction point sources are shown as red dots (and the stat e jurisdiction point sour ces are shown as green dots). Some addition federal and state jurisdictio n point sources located in the rural areas of Mexicali are not shown on Figure 2-1. ICAR made the initial data request to SE MARNAT for existing information regarding emissions for the year 2005 from Mexicali fede ral jurisdiction point sources. SEMARNAT provided two electronic files in Excel format: “Empresas Comp Federal (EMIS 230108).xls” and “DATGEN_MEXICALI_05.xls”. The “Empre sas Comp Federal (EMIS 230108).xls” file provided a listing of all federal ju risdiction sources located in the st ate of Baja California, as well as those federal jurisdiction sources that reported 2005 emissions to SEMARNAT. The “DATGEN_MEXICALI_05.xls” file consisted of several sheets of which the following were most relevant for the compilation of emissions:

• Hoja1 – Complete facility description;

• EqComb – Emissions from combustion equipment;

• ActProc – Emissions from non-comb ustion equipment and processes;

• Chim – Stack parameters; and

• EqCont – Description of control equipment. In general, the information contained in the “DATGEN_MEXICALI_05.xls” file was fairly complete. However, much of the data cont ained in this file was represented in codes,

2005 Mexicali Emissions Inventory 

### 2-2 Final, 2/27/09

### Figure 2-1. Location of Fede ral and State Jurisdiction Point Sources in the Urban Portion of Mexicali

2005 Mexicali Emissions Inventory 

### 2-3 Final, 2/27/09

### abbreviations, and acronyms. As a result, ICAR made a subsequent request to SEMARNAT to provide the relevant data dicti onaries needed to decipher the file. Subsequently, SEMARNAT provided two data dictionary files associated with the “DATGEN_MEXICALI_05.xls” file: “Diccionario_Datos_Datgen.xls” and “Codigos_catalogo.xls”. The “Diccionario_Datos_Datgen.xls” fi le provided the data dictionary and formats used by the “DATGEN_MEXICALI_05.xls” file, while the “Codigos_catalogo.xls” file provided a key for all of the codes, abbreviations and acronyms. Using the “Empresas Comp Fe deral (EMIS 230108).xls” and “DATGEN_MEXICALI_05.xls”, along with the data dictionary files, ICAR compiled emissions from the federal jurisdiction point sources.
2.2 State Jurisdiction Point Source Methodology There are 173 state jurisdiction point sources located in the m unicipality of Mexicali. As seen in Figure 2-1, many of these state jurisdiction point sources are cl ustered in the city’s industrial parks, which was conf irmed by SEMARNAT. The names for these facilities are listed in Table 2-1. ICAR made an initial request to SPA for existing informati on regarding emissions for the year 2005 from Mexicali state jurisdiction point sour ces. SPA provided two electronic files: “2­ REGISTROS (2005).xls” and “SIARE.mdb”. The “2-REGISTROS (2005).xls” file provided a listing of all companies that submitted 2005 emissi on estimates to SPA and identified the paper file location within SPA offices of these emi ssion submittals. The “SIARE.mdb” file is an Access database which contains such information as:

• Facility name

• File number

• Inventory year

• Type and number of process that generate emissions

• Emission controls (if any)

• Pollutant type (in units of kg/hr and kg/year)

2005 Mexicali Emissions Inventory 

### 2-4 Final, 2/27/09Table 2-1. Listing of State Jurisdiction Point SourcesFacility NameAccuride International, S.A. de C.V. (Nelson 6)Facility NameCervecería de Baja California,S.A. de C.V.Facility NameExpopartes, S.A. de C.V.Accuride International, S.A. de C.V. (Pta-Oeste, Nelson-12)Chromalloy, S.A. de C.V.(Planta 1)Fabricaciones y Montajes Metálicos, S.A. de C.V.Agribrands Purina México, S.A. deC.V.Chromalloy, S.A. de C.V.(Planta Ii)Fanosa,S.A. de C.V.Alimentos y Frituras de Baja California, S.A. de C.V.Cias. Periodísticas del Sol de Pacífico, S.A. de C.V.Femsa, S. de R.L. de C.V.American Meter de Mexico, S.A. de C.V.Compre Block Sucursal CribaFetasa Culiacán, S.A. de C.V.Amp Industrial Mexicana, S.A. de C.V.Compre Block, S.A. de C.V.Fetasa Industrial, S.A. de C.VArrendadora del Colorado del Norte, S.A. de C.V.Con Mex de Mexicali, S.A.Fetasa Mexicali, S.A. de C.V.Ascotech, S.A. de C.V.Concretos Apasco, S.A. de C.V.Flexcel-Mexicali, S.A. de C.V.Asociacion Rural de Interes Colectivo de Baja California  ́80 deR.I.Concretos Bajanor de Mexicali, S.A. de C.V.Frituras de Mexicali, S.A. de C.V.Benq Mexicana, S.A. de C.V.Constructora Gallego, S.A. de C.V. (Planta1)Fruvemex Mexicali, S.A. de C.V.Bi Technologies, S.A. de C.V.Constructora Gallego, S.A. deC.V. (Planta 2)FurukawaMexico, S.A.de C.V.Bimbo, S.A. de C.V.Constructora Gusa, S.A. de C.V.Grayline de México, S.A. de C.V.Black & Decker Hhi México, S. de R.L. deC.V. (Pta-2)Controles de Mexicali, S. deR.L.Grupo Industrial Persal, S.A. de C.V.Black & Decker Hhi México, S. de R.L. de C.V. K1Dafmex, S. deR.L. de C.V.Guaycan Quimicosy Petroliferos, S.A. de C.V.Black & Decker Hhi México, S. de R.L. de C.V. K2Dataproducts Imaging Solution, S.A. deC.V.Harinera de Maíz de Mexicali, S.A.de C.V.Black & Decker Hhi México, S. de R.L. de C.V. P1Delivamex, S. de R.L. de C.V.Helados y Mezclas, S.A. de C.V.Block Centinela, S.A. de C.V.Display Orion Mexicana, S.A. de C.V.Honeywell Aerospace de México, S.A. de C.V.Bloquera Moderna, S.A. de C.V.Eco Constructora, S.A. de C.V.Honeywell Productos Automotrices, S.A. de C.V.(Radiadores)Blue Star, S.A. de C.V.Electronica Vanguard, S.A. de C.V.Honeywell Productos Automotrices, S.A. de C.V.(Turbos)Breg México, S. de R.L. deC.V.Embotelladora de Mexicali,S.A. de C.V.Hykam Electronica de Mexico, S.A. de C.V.Cajas Agricolas, S.A.de C.V.Emermex, S.A de C.V.Ideas Y/O Enrique Hernández SantiagoCalidad Internacional de Pulido, S.A. deC.V.Emermex, S.A. de C.V. (Edif-3)Iluminación Cooper de Las Californias, S. de R.L. deC.V.Carley de México, S.A. de C.V.Empaques de Cartón TitánImpresorayEditorial, S.A. de C.V.Cd Electrónica de Mexico, S.A de C.V.Empresas L.M., S. de R.L. de C.V.Industria deRefrescos del Noreste,S. de R.L. de C.V.Cementos Químicos Industriales,S.A. de C.V.Energéticos y Grasas del Norte, S.A. de C.V.Industrial Vallera de Mexicali, S.A. de C.V. (CotoTechnologies)Cemex Concretos, S.A. deC.V.Ensambladores Electrónicos de México, S.A.Industrial Vallera de Mexicali, S.A. de C.V. (Div.Vishay)Cemex México, S.A. deC.V.Ensambles de Mexicali, S.A. de C.V.Industrial Vallera de Mexicali, S.A.de C.V.(Loveday)Centro Camionero de Mexicali, S.A. de C.V.Ensambles de Silleria Mexicana, S.A. de C.V.Industrial Vallera de Mexicali, S.A. de C.V. (ManikMotors)

2005 Mexicali Emissions Inventory 

### 2-5 Final, 2/27/09Table 2-1. ContinuedFacility NameFacility NameFacility NameIndustrial Vallera de Mexicali, S.A. de C.V.(Martech-Mdi West)Mextearinas, S.A. de C.V.Sabritas,S. de R.L. de C.V.Industrial Vallera de Mexicali, S.A. deC.V. (Másimo)Molinerea del Valle, S.A. de C.V.Sánchez Quiñónez César (Servicios y AcabadosIndustriales)Industrial Vallera de Mexicali, S.A. de C.V. (Merchants Metals)Muebles y Diseños El Bajio,S.A. de C.V.Sds de Mexico, S.A. deC.V.Industrial Vallera de Mexicali, S.A. de C.V. (Taymar Plastics)Navarrete Rayos Noel AlejandroService Container de Baja, S. deR.L. de C.V.Industrias SL, S.A. de C.V.North Safety de Mexicali, S. de R.L.de C.V.Sigma Lapos, S.A. de C.V.Industrias Zahori, S.A. de C.V.Novamex México, S.A. deC.V.Sigma Lapos, S.A.de C.V. (Sucursal)Inovación en Concretos, S.A. de C.V.NSF Mexicali, S. de R.L. de C.V.Skyworks Solutions, S.A. de C.V.International Assembly Specialists, S.A. de C.V.Nutricion Maxima de Baja California, S.A. de C.V.Solís Meraza María de LaCruz (Transformadora deMaderas Satélite)Isoclima de México, S.A. de C.V.Nutrimentos Mexicanos, S.A. de C.V.SPG deMéxico, S.A. de C.V.Isoclima de México, S.A. de C.V.Omega Industries, S.A. de C.V.Stone Container de México, S. de R.L. de C.V.J. Cox de México, S.A. de C.V.Operaciones de Clase Mundial, S.A.de C.V.Strategic Materials Mexicana, S.A. de C.V.Jean Charles Mexicali, S.A. de C.V.Orthodental,S.A. de C.V.Sukarne Produccion, S.A. de C.V.Jumex Mexicali, S.A. de C.V.Panasonic Electric Works Mexicana,S.A. de C.V.Suntek Manufacturing Technologies, S.A. de C.V.Kang Seo Mexicana, S.A. de C.V.Partes Remanufacturadas deMéxico, S.A. de C.V.Super Muros, S.A. de C.V.Kb Foam de Mexicali, S.A.de C.V.Pegamentos Industriales Galileo, S.A. de C.V.TechnologiesSolutions And Services, S.A. de C.V.Kerns Beverages de México, S. de R.L. de C.V.Pims, S.A. de C.V.Tecnologías Internacionales de Manufactura, S.A. deC.V.Kwang Sung Electronics México, S.A. de C.V. (Pta-1)Placas Termodinámicas, S.A. de C.V.TecnomexIndustrial, S.A. de C.V.Kyowa Mexicali, S.A. de C.V.Ponce Elizalde Armida (Transformadora de Maderas Tramex)Tecnomex Industrial, S.A. de C.V.L.G. Innotek Mexicali, S.A. de C.V.Procesadora y Empacadora deCarnes del Norte, S.A. de C.V.Temcomex, S.A. de C.V.Lam-Mex, S.A.Prodofi, S. de R.L. de C.V.Teradyne Connection Systems de México, S.A. deC.V.Lancer Orthodontics de México, S.A. de C.V.Productos de Acero de Baja,S.A. de C.V.ThomsonLechera Mexicana, S.A. de C.V.Productos de Barro, S.A. de C.V.Tri-State deMexico, S.A. de C.V.LG Electronics Mexicali, S.A. de C.V.Productos Hola del Noroeste, S.A. de C.V.Tubos de Acero Internacional, S.A. de C.V.L-N Safety Glass, S.A. de C.V.Productos Urólogos de Mexico, S.A de C.V.Valutech Out Sourcing, S.A. de C.V.Lopez Cazares RafaelPromotora Industrial Per, S.A. de C.V.Veladoras del Pacifico, S.A. de C.V.Lopez Cazares Rafael (Rancho)Pulidora de Baja California, S.A. de C.V.Vimifos, S.A. de C.V.Maquiladora San Diego, S.A. de C.V.Pulidora Los Ángeles, S.A. de C.V.Wabash Technologies deMéxico, S. de R.L. de C.V.Marko de México, S. de R.L. de C.V.Puligamex, S.A. de C.V.Yiro Fabrica,S. de R.L. de C.V.Masterwork Electronics de México, S.A. de C.V.RheemMexicali, S. de R.L. de C.V.Zahori, S.A. de C.V.Megaplast, S.A. de C.V.Robert Bosch Tool deMéxico, S.A. de C.V.

2005 Mexicali Emissions Inventory 

### 2-6 Final, 2/27/09

### • Type of fuel combusted

• Number of stacks It should be noted that emissions were reported at the facility-level, but not at the process- or equipment-level. Also, stack pa rameters (i.e., stack coordinates, stack height, stack diameter, exit gas velocity, and exit gas temperature) we re not included in the “SIARE.mdb” file. Based upon information contained in these two files, ICAR developed an Excel spreadsheet (“state_point_source_R0.xls”) that compiled the following data for each facility:

• Facility name

• Facility identification number (ID)

• Main activity

• Facility address

• Facility point of contact

• SCIAN code

• Emission quantific ation (kg/year)

• Type of control

• Facility latitude

• Facility longitude

• Emission points (i.e., the quantity and type of processes that generate emissions)

• Material type (i.e., type of fuel used)

• Basis of emissions estimate. All of these data were based upon informa tion obtained from the “SIARE.mdb” file.
2.3 Results The federal jurisdiction point source emi ssions are shown in Table 2-2. Some conclusions that can be drawn from Table 2-2 are that the power pl ants are the largest sources of NO x 

### , CO, and PM 10 

### . The PEMEX facility is the larges t source of VOC emissions, while the

2005 Mexicali Emissions Inventory 

### 2-7 Final, 2/27/09

### glass plants (i.e., Fevisa and Vitro AFG) and the paper plan t (i.e., Fábrica de Papel San Francisco) are the three largest sources of SO 2 

### . It should be noted th at there are no emissions reported from the Federal Electric Commissi on (Comisión Federal de Electricidad-CFE) geothermal power plant in Cerro Prieto. Emissions for the 173 state jurisdiction point sources are shown in Table 2-3, by industry group. From Table 2-3, it is clear that the Petr oleum and Coal Products Manufacturing sector is the largest emitter of SO 2 

### and a significant contributor of NO x 

### . The Other Industrial Manufacturing sector has the largest NO x 

### emissions. Computer, Co mmunication, and Electronic Manufacturing is the largest em itter of VOC, while Food Manufact uring and Primary Metals are the primary contributors of CO emissions. Emissions for PM 2.5 

### , NH 3 

### , and CH 4 

### are not included in Table 2-2 because emissions of these pollutants were not estimated by SPA.
2.4 Quality Assurance
After compiling emissions for federal jurisdiction and state jurisdiction point sources, ICAR conducted quality assuranc e (QA) of these emissions. ICAR’s QA of the federal jurisdiction point sour ces used a QA checklist that focused on the following aspects:

• Facility information (i.e., North Ameri can Industry Classifica tion System [NAICS] codes, address, latitude and longitude)

• Process/equipment information (i.e., pr ocess/equipment description, source classification code [SCC], stack assignment)

• Emissions (i.e., pollutant completeness, em issions contribution, particulate matter, hydrocarbons, emission units, emission reasonableness)

• Stack parameters (i.e., stack height, stack diameter, exit gas temperature, exit gas velocity, stack latitude and longitude)

• Operating schedule (i.e., days per week, hours per day)

• Controls (i.e., control device, control efficiency) Overall, ICAR’s QA evaluation indicated fairly complete data for the federal jurisdiction point sources. However, a few specific data elements needed to satisfy the required characteristics of the Mexicali inventory were missi ng from some or all of the federal jurisdiction

2005 Mexicali Emissions Inventory 

### 2-8 Final, 2/27/09

### Table 2-2. 2005 Mexicali Federal Jurisd iction Point Source Emissions (Tons per Year) Facility Name NO x SO 2 VOC CO PM 10 PM 2.5 CFE Central Turbo Gas Mexicali 27.4 11.8 1.9 2.4 CFE Campo y Centrales Geothermoeléctrico de Cerro Prieto Dafmex, S. de R.L. de C.V. 0.4 0.1 0.0 0.3 0.0 0.0 Energía Azteca X, S. de R.L. de C.V. 5,757.9 7.5 12.4 1,433.2 548.4 Energía de Baja California, S. de R.L. de C.V. Central Termoeléctrica 2,385.5 3.1 5.1 593.8 227.2 Fevisa Industrial, S.A. de C.V. 277.4 2,500.2 0.5 29.0 140.2 91.6 Fábrica de Papel San Francisco, S.A. de C.V. 206.8 788.6 0.5 80.6 44.7 29.3 Infra, S.A. de C.V. Sucursal Mexicali 0.2 Kyowa Mexicali, S.A. de C.V. 0.1 0.0 76.3 0.1 0.0 0.0 Kenworth Mexicana, S.A. de C.V. 9.4 1.8 75.6 1.3 0.1 0.1 Maquiladora San Diego, S.A. de C.V. 0.9 0.4 0.2 30.0 2.4 2.2 PEMEX Refinación Planta Mexicali 458.9 Quimical, S.A. de C.V. 0.0 0.0 0.0 0.0 0.0 0.0 Termoeléctrica de Mexicali, S. de R.L. de C.V. 4,664.2 6.1 10.1 1,161.0 444.2 Vitro AFG, S.A. de C.V. 601.4 330.1 19.4 233.9 128.0 5.1 Fundiciones San Martin o Felix Jesus del Valle Zamora 0.2 1.1 0.1 71.1 0.0 0.0 Total 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 

### Table 2-3. 2005 Mexicali Stat e Jurisdiction Point Source Emissions (Tons per Year) 3-Digit NAICS NAICS Description NO x 

### SO 2 

### VOC CO PM 10



### 212 Mining (metallic and nonmetallic) 1.47 0.00 0.00 0.09 12.92 311 Food industry 70.11 60.84 0.00 243.61 22.53 312 Beverages/tobacco industry 1.80 0.00 0.00 0.23 0.00 321 Wood products industry 0.00 0.00 0.00 0.00 0.72 322 Paper industry 5.58 0.00 3.35 2.67 2.80 323 Publishing industry 0.00 0.00 0.14 0.00 0.00 324 Petroleum and coal products manufacturing 133.76 732.91 0.00 10.07 2.71 325 Chemical industry 0.31 0.00 0.00 0.03 0.00 326 Plastics and rubber industry 27.22 116.90 2.08 9.03 7.97 327 Nonmetallic materials manufacturing 11.04 0.12 4.48 7.06 3.27 331 Primary metals industry 11.31 19.95 0.00 121.58 0.24 332 Metal products manufactur ing 4.81 0.00 7.64 16.05 6.89 333 Equipment and machinery manufacturing 5.94 0.00 2.36 7.48 0.48
334 Computer, communication, electronics manufacturing 2.13 0.19 33.98 16.09 9.45 335 Electricity generation equipment manufacturing 0.00 0.00 0.03 0.00 0.02 336 Transportation equipment manuf acturing 0.71 0.00 1.42 12.90 12.28 337 Furniture manufacturing 1.99 0.00 14.53 6.06 1.28 339 Other industrial manufacturing 157.76 0.00 3.22 26.85 5.99 493 Storage service 8.02 42.27 0.00 0.08 0.00 Total 443.97 973.19 73.23 479.88 89.55

2005 Mexicali Emissions Inventory 

### 2-9 Final, 2/27/09

### point sources. The first data element was pro cess/equipment SCC, which was missing from all sources; this was gap filled using engineering judgment based upon the specific process equipment description and fuel t ype. The stack latitude and l ongitude coordinates were also missing from a number of the sources. In most cases, these coordinates were for sources with a large number of processes that typically did not have well-defined stacks (e.g., vents, etc.). For these sources, the facility latitude and longitude coordinates were assi gned to the stack(s). The most significant data problem identif ied by ICAR’s QA evaluation of the state jurisdiction point sources was that none of the facilities had process- or stack-level emissions. After identifying this issue, ERG and ICAR focused on the seven most significant state jurisdiction point source emitters (i.e., Bim bo, Chromalloy, Sabritas, Sukarne, Temcomex, Thomson, and Zahori). ICAR searched the SPA pa per files to obtain process- and stack-level emissions for these seven sources. Data obt ained from the SPA paper files included the following:

• Number of sources

• Process/equipment description and codes

• Stack assignment

• Emission units

• Stack coordinates

• Stack height

• Stack diameter

• Exit gas velocity

• Exit gas temperature

• Operating schedule (days/year, hours/day)

• Controls (device and efficiency) In addition to the detailed data collected fo r the seven most significant state jurisdiction point source emitters listed above, similar proces s- and stack-level data from the SPA files were

2005 Mexicali Emissions Inventory 

### 2-10 Final, 2/27/09

### subsequently researched for another 85 state jurisdiction point sources. However, project constraints prevented similar data collection for the remaining 81 facilities. Two of the five data quality objectives (DQOs) for the Mexicali inventory project are applicable to point source emi ssions. These are as follows:
1. Identification of 80% of a ll federal jurisdiction and stat e jurisdiction point sources located within the municipality of Mexicali. Acceptance criteria: A list of poin t sources under the federal (SEMARNAT Delegation), and state (SPA) jurisdictions co mpares to the emissions data, by source, provided by those agencies. 2. Point sources: Emissions estimates with an accuracy of ± 25% for at least 10% of the facilities. Acceptance criteria: Estimated emissions fo r 10% of the total fa cilities compare to measured emissions of all pollutants w ith ± 25% accuracy. The emissions data measurements will be obtained from subcontractor ICAR, and will be based on data collected over the past five years in Mexicali. Based on the federal jurisdiction and state jurisdiction point so urce methodologies and quality assurance used, both DQOs were met. With regard to DQO #1, all federal jurisdiction and state jurisdiction point source s were included. The lists of federal jurisdiction and state jurisdiction point sources were checked multiple times with SEMARNAT and SPA to ensure completeness. For DQO #2, ICAR checked process- and stack-level data obtained from SEMARNAT electronic file s (for federal jurisdiction sources) and from SPA paper files (for state jurisdiction sources) against information they had previously obtained from their industrial clients (2 federal jurisdiction sources and 38 state jurisdiction sources). Ov erall, emissions data were within ± 25% accuracy.

2005 Mexicali Emissions Inventory 

### 2-11 Final, 2/27/09

# 3.0 AREA SOURCES

Area sources are sources that are too numerous and dispersed to be effectively included in the point source inventory. Area sources include some commercial and/or industrial sources such as autobody shops and dry cleaners, fugitive sources such as paved and unpaved road dust, and fuel combustion in small boilers. The me thodology used to estimate the 2005 Mexicali area source emissions is described below.
3.1 Methodology
Wherever possible, local activity data were collected in Mexicali and used to estimate area source emissions. Local activity data collection was facilitat ed by ICAR, who are conveniently located in Mexicali. ICAR contacted some local sour ces of activity data directly (e.g., PEMEX, fire departments, etc.); in othe r situations, ICAR worked through SPA to reach these contacts (e.g., natural ga s and LPG distributors, Depa rtment of Health, etc.). If local activity data could not be collected from local contacts, then data from publicly available government agencies were used. In addition, some methodologies and data from the development of the 1999 Mexico NEI and recent emissions inventory work conducted in 2007 in San Luis Río Colorado, Sonora and the northeastern (agricultural) portion of Mexicali were also used (ERG, 2006; ENVIRON and ERG, 2008). General estimation methodologies for relate d groups of area s ource categories are described below and summarized in Table 3- 1. Detailed documentation for each area source category is provided through the Area Source Categor y Forms, which are located in Appendix A. The Area Source Category Forms succinctly summarize the following for each source category: category description, pollutants, method, activity data, emission factors, notes and assumptions, emission calculations, and spat ial allocation surrogates.

2005 Mexicali Emissions Inventory 

### 3-1 Final, 2/27/09Table 3-1. General Emission Estimation Methodologies forArea Sources in MexicaliSource CategoryPollutantsMethodologyActivity DataSpatial AllocationNotesIndustrial Fuel Combustion –Distillate & Residual FuelOil, Natural Gas, LPGNOx, SO2, VOC, CO,PM10, PM2.5, CH4AP-42 emission factorsLocal use statisticsEmployee data bysectorReconciled with pointsources.Commercial/InstitutionalFuel Combustion – NaturalGas & LPGNOx, SO2, VOC, CO,PM10, PM2.5, CH4AP-42 emission factorsLocal use statisticsEmployee data bysectorResidential Fuel Combustion– Natural Gas & LPGNOx, SO2, VOC, CO,PM10, PM2.5, CH4AP-42 emission factrsLocal use statisticsHousehold dataArchitectural CoatingVOCMexico-specific per capitaemission factorsPopulationPopulationEmissions estimated forsolvent- and water-basedcoatings.Industrial Surface CoatingVOCMexico-specific peremployee emission factorsEmployment databy sectorEmployee data bysectorSeven subcategories.Reconciled with pointsources.Autobody RefinishingVOCMexico-specific peremployee emission factorsEmployment dataEmployee dataGraphic ArtsVOCMexico-specific per capitaemission factorsPopulationPopulationDry CleaningVOCMexico-specific peremployee emission factorsEmployment dataEmployee dataPetroleum-based solventsonly; no perchloroethyleneDegreasingVOCEIIP per employee emissionfactorsEmployment databy sectorEmployee dataFour subcategories.Reconciled with pointsources.Consumer SolventsVOCEIIP per capita emissionfactors (slightly adjusted)PopulationPopulationU.S. emission factors –household and automotivefactors modified torepresent Mexicanconditions.Traffic MarkingsVOC1999 Mexico NEI resultsN/APaved roadnetworkLocal applied quantitieswere unavailable.Used information from1999 Mexico NEI.Asphalt ApplicationVOC1999 Mexico NEI resultsN/APaved roadnetworkLocal applied quantitieswere unavailable.Used information from1999 Mexico NEI.

2005 Mexicali Emissions Inventory 

### 3-2 Final, 2/27/09Table 3-1. ContinuedSource CategoryPollutantsMethodologyActivity DataSpatial AllocationNotesPetroleum Transport andDistributionVOCEIIPemissionfactorsLocal fuelthroughputPopulationStage I controls(submerged with vaporbalance; no Stage IIcontrols)Agricultural BurningNOx, SO2, VOC, CO,PM10, PM2.5ARB emission factorsBurned acreageAgricultural landuse dataBurning limited to wheat,sorghum, and weeds inirrigation canals andditchbanks.Agricultural TillingPM10, PM2.5ARB emission factorsPlanted acreageAgricultural landuse dataAgricultural HarvestingPM10, PM2.5ARB emission factorsHarvested acreageAgricultural landuse dataAgricultural PesticidesVOCEIIP emission factorsHarvested acreage,type and quantityof pesticidesappliedAgricultural landuse dataPesticide application rateslimited to eight crop typesBeef Cattle FeedlotsPM10ARB emission factorsNumber ofslaughtered cattleAgricultural landuse dataLivestock VOCVOCARB emission factorsNumber oflivestock headAgricultural landuse dataLivestock AmmoniaNH3WRAP NH3model emissionfactorsNumberofheadAgricultural landuse dataFertilizer ApplicationNH3WRAP NH3model emissionfactorsPlanted acreage,type and quantityof fertilizersappliedAgricultural landuse dataFertilizer application rateslimited to eight crop typesPaved Road DustPM10, PM2.5AP-42 emission factorequationLocal VKT, siltloading, vehiclespeed, precipitationPaved roadnetworkVKT to be developed inconjunction with motorvehicle emissions/consideration of siltloading samplesUnpaved Road DustPM10, PM2.5AP-42 emission factorequationLocal VKT, siltcontent, moisturecontent, vehicleweight and speed,precipitationUnpaved roadnetworkVKT to be developed inconjunction with motorvehicle emissions/consideration of siltcontent and moisturecontent samplesNOx, VOC, CO, PM10,Number of housesStructural FiresPM2.5, CH4EIIP emission factorsburnedPopulation

2005 Mexicali Emissions Inventory 

### 3-3 Final, 2/27/09Table 3-1. ContinuedSource CategoryPollutantsMethodologyActivity DataSpatial AllocationNotesBorder Crossing (idlingvehicles from Mexicali toCalifornia)NOx, SO2, VOC, CO,PM10, PM2.5, NH3MOBILE6-Mexicoemission factorsLocal trafficcounts, idling timesBorder crossingEmission factors fromMOBILE6-MexicoBrick KilnsNOx, SO2, VOC, CO,PM10, PM2.5Utilization of previousinventoryNot applicableBrick kiln locationsCharbroiling/Street VendorsNOx, SO2, VOC, CO,PM10, PM2.51999 Mexico NEI resultsN/APopulationLocal data wereunavailable. Usedinformation from 1999Mexico NEI grown basedon population.Construction DustPM10, PM2.51999 Mexico NEI resultsN/APopulationLocal data wereunavailable. Usedinformation from 1999Mexico NEI grown basedon population.LandfillsVOC,CH4AP-42emissionfactorsAnnual quantity ofwaste landfilled,opening yearLandfill locationWastewater Treatment PlantVOCU.S. EPA emission factorWastewater flowratesPlant locationLocal data unavailable torun model.

2005 Mexicali Emissions Inventory 

### 3-4 Final, 2/27/09

### 3.1.1 Fuel Combustion Categories Fuel combustion categories in clude industrial fuel combus tion, commercial/institutional fuel combustion, and residential fuel combustion. Wherever possible, local collected data were used to estimate emissions. If local data were unavailable, publicly available government fuel statistics at the national- and regional-lev el were disaggregated down to the Mexicali municipality.
Industrial fuel combustion includes distillate fuel oil, residual fuel oil, liquefied petroleum gas (LPG), and natural gas while th e commercial/institutional and residual fuel combustion source categories are limited to LPG a nd natural gas. Attempts were made by SPA and ICAR to contact all of the Mexicali fuel distributors. Natu ral gas statistics were obtained from Ecogas Mexico, S. de R.L. de C.V. (Vi vo, 2008). Four LPG distri butors were contacted; three of these distributors pr ovided 2005 sales information (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008). Sales information from the three LPG distributors that provided information were averaged together and then app lied to the LPG distributor that did not provide information. The PEMEX terminal in Mexicali provided sales information re garding gasoline and distillate fuel (including motor ve hicle, agricultural, and industri al uses), but did not respond to requests for information regarding residual fuel oils (Parra Armenta, 2008 ). Residual fuel oil information was obtained from the PEMEX terminal in Rosarito, which markets residual fuel oil in Mexicali (Miranda Palazuelos, 2008). Biomass combustion appears to be virtua lly nonexistent; most heating during the mild winters is electric he ating (Martinez, 2007).
3.1.2 Evaporative VOC Categories Evaporative VOC categories include the following area source categories: architectural surface coating, industrial surf ace coating, degreasing, graphic arts, traffic markings, dry cleaning, consumer products, asphalt app lication, and gaso line distribution. The development of Mexico-specific emission f actors as part of the 1999 Mexico NEI for architectural surface coating, i ndustrial surface coating, graphic arts, and dry cleaning was a

2005 Mexicali Emissions Inventory 

### 3-5 Final, 2/27/09

### significant advance. These emission factors were based upon data from the National Association of Paint and Dye Manufacturers (A sociación Nacional de Fabrican tes de Pinturas y Tintas – ANAFAPYT) and the National Chamber of the Dr y Cleaning Industry (Cámara Nacional de la Industria de Lavanderías – CANAL AVA) (ANAFAPYT, 2003; ANAFAPYT, 2004; CANALAVA, 2002). These Mexico -specific emission factors were combined with Mexicali population and sector-specific employee counts. The industrial surface coating emissions were also reconciled with emissions from poin t source industrial surface coating operations Because Mexico-specific emission factors were not developed for degreasing and consumer products, per capita and per employ ee emission factors were obtained from the Emission Inventory Improvement Program (EIIP) guidance documents (EIIP, 1996; EIIP, 1997). However, recent health survey data collected in San Luis Río Colorado, Sonora indicate lower usage of household products corresponding to incr eased usage of bleach a nd a virtual absence of windshield fluid (Fierro and O’Rourke, 2007; Fierro, 2007; G onzalez-Ayala, 2007); the EIIP emission factors were adjusted to account for these findings. The degreasing emissions were also reconciled with emissions from point source degreasing operations. Traffic markings and asphalt application qua ntities were requested from the Mexicali Public Works Department; however, these quant ities were not available (Reynoso Ambriz, 2008). As a result, the emission estimates for tra ffic markings and asphalt application from the 1999 Mexico NEI were used for th e municipality of Mexicali.
Gasoline sales statistics were obtained fr om PEMEX (Parra Armenta, 2008), while gasoline distribution emission factors were obtained from EIIP guidance (EIIP, 2001a).
3.1.3 Agricultural Categories The northeast portion of Mexicali has significa nt agricultural activity. Activity data for four specific agricultural categories (i.e., pe sticides, burning, tilling, and harvesting) were requested from both the state and federal agricu ltural agencies. These four agricultural categories were previously estimated for th e 2005 San Luis Río Colorado and northeastern Mexicali inventory (ENVIRON and ERG, 2008). Or iginally, it was anticip ated that improved information could be collected from the state and federal agricultural agencies for Mexicali; however, the only useful agricultural informa tion that could be obtained was estimates of

2005 Mexicali Emissions Inventory 

### 3-6 Final, 2/27/09

### burning of wheat residues, sorghum residues, and weeds in irrigati on canals and drainages (Rodríguez Hernández, 2008; López Bojorques, 2008). Agricultural burning methodologies from the California Air Resources Board (ARB) were used to estimate emissions (ARB, 2005). Crop acreage information was provided for the agricultural tilling and harvesting source categories; however, this information had previo usly been obtained from a government database (SAGARPA, 2007a). For the previous invent ory work in San Luis Río Colorado and northeastern Mexicali, m unicipality-level emissions for agri cultural tilling and harvesting were spatially allocated in order to estimate the portion of emissions that were contained within that project’s modeling domain (ENVIRON and ERG, 2008). For the 2005 Mexicali inventory, the previously estimated municipality-level emi ssions were simply used without the modeling domain spatial allocation. The previous agricultural tilling and harvesting estimates were based upon ARB methodologies (ARB, 2003a; ARB, 2003b). Emissions from agricultural pesticides we re estimated by applying pesticide-specific emission factors to the quantities of pesticid es applied. Typical crop-specific pesticide application rates were obtained from state- specific cost production estimates (SAGARPA, 2007b). However, the application ra tes were only available for a limited number of crops (i.e., garlic, green alfalfa, cotton, broccoli, asparagus, tomatoes [green and red], and wheat). These pesticide application rates were applied to th e crop-specific planted acres. specific pesticide application rates. Pesticide emission factors were obtained fr om EIIP guidance (EIIP, 2001b). Active ingredients were identified based upon Mexico’s Registry of Authorized Pesticides (COFEPRIS, 2007). Formulation types were also assumed based upon the predominant formulation types listed in the Registry. Active an d inactive ingredient fractions, as well as inert ingredient VOC fractions, were obtained from th e EIIP guidance. Pesticide information (i.e., density, % active ingredient, and vapor pre ssure) was obtained from various databases maintained by California’s Department of Pesticide Regulation (DPR, 2007). Beef cattle feedlots are a source of fugitive dust caused by cattle movement over dusty ground. Slaughter statistics were used to estimate the number of cattle in feedlots. There are approximately 11 feedlots in Mexicali, all lo cated outside of the urban area (Rodríguez Hernández, 2008). It has been estimated that ca ttle stay in the feedlots for approximately 90

2005 Mexicali Emissions Inventory 

### 3-7 Final, 2/27/09

### days prior to being slaughtere d (Sukarne, 2008). The beef cattle feedlot emission factor was obtained from ARB (ARB, 2004). VOC emissions from livestock (i.e., beef cattle, dairy cattle, swine, sheep, and goats were also estimated. State- and di strict-level livestock headcount s were obtained from a SAGARPA database and then allocated to the municipality-level based on slaughter statistics (SAGARPA, 2007c). The livestock VOC emission factors were obtained from ARB (ARB, 2004).
3.1.4 Ammonia Categories Two important ammonia area source categorie s in a regional inventory are typically livestock ammonia and agricultura l fertilizers. State- and dist rict-level livestock headcounts were obtained from a SAGARPA da tabase and then allocated to the municipality-level based on slaughter statistics (SAGARPA, 200 7c). The livestock ammonia emission factors were based on information gathered from literature reviews that is currently being implemented in the latest Western Regional Air Pa rtnership (WRAP) NH 3 

### modeling (Chinkin et al., 2003). Emissions from agricultural fertilizers we re estimated by applying fertilizer-specific emission factors to the quantities of fertilizer applied. Similar to agricultural pesticides, typical crop-specific fertilizer applic ation rates were obtained from state-specific cost production estimates (SAGARPA, 2007b). As with the pestic ides, application rates were only available for a limited number of crops (i.e., garlic, green alfa lfa, cotton, broccoli, asparagus, tomatoes [green and red], and wheat). Ammonia emission estimates from fertilizer applic ation were developed using emission factors from the European Envi ronment Agency (EEA, 2002) as recommended in the development and application of the WRAP NH 3 

### model.
3.1.5 Paved Road Dust Paved road dust emissions were estimated usi ng the most recent emission factor equation presented in the U.S. EPA Compilation of Ai r Pollutant Emission Factors (AP-42), Section 13.2.1 (revised November 2006) (U.S. EPA, 1995). Silt samples were collected at 10 locations in Mexicali by ICAR in July 2008. The 10 locat ions are shown in Figure 3-1 (numbered 1 through 10) and are as follows:

• 1 – Calzada Lázaro Cárdenas between Cal zada Benito Juárez and Calzada Venustiano Carranza

2005 Mexicali Emissions Inventory 

### 3-8 Final, 2/27/09Figure 3-1. Location of Paved andUnpaved Road Dust Sampling Sites

2005 Mexicali Emissions Inventory 

### 3-9 Final, 2/27/09

### • 2 – Calzada Independencia between Boulev ard Adolfo López Ma teos and Calle Río Culiacán

• 3 – Calzada Industrial Palaco between Calzada M. Gómez Morin and Calzada Lázaro Cárdenas

• 4 – Francisco I. Madero between Calle Justo Sierra and Calle “F”

• 5 – Calzada Anahuac between Avenida Torino and Avenida Gobernador

• 6 – Avenida Oaxaca between Calle Uxmal and Heroico Colegio Militar

• 7 – Calle Río Mocorito between Calzada Cu auhtémoc and Avenida F. Montes de Oca

• 8 – Calzada Laguna de Xochimilco be tween Calzada Anahuac and Granada

• 9 – Avenida Río Verde between Calle Cuarta and Calle Novena

• 10 – Avenida Centauro del Norte between Calzada Lázaro Cárdenas and Calzada Héctor Terán Terán All samples were collected according to the sampling procedures presented in Appendix C.1 of AP-42 (U.S. EPA, 1995). The samples were analyzed by a laboratory in Tijuana, Laboratorio de Muestreo y Análisis, S.A. de C.V., according to the analytical procedures presented in Appendix C.2 of AP-42 (LMA, 2008a ). The paved road dust emission factors (PM 10 

### and PM 2.5 

### ) were calculated using the AP-42 methodology; emission factors were calculated for each sampling site and then averaged together. A precipitation adjustment factor of 0.98562 corresponding to 21 “wet” days was also used (NWS, 2005). The vehicle kilometers traveled (VKT) esti mate used to estimate paved road dust emissions was developed in conjunction with th e on-road mobile source VKT development (see Section 4.0 of this report for de tails). Figure 3-2 demonstrates a portion of the analysis to determine the split of paved and unpaved analysis ; further details are located in Appendix B, Section VI, of this report. The estimated pave d road dust emission factors were then combined with the link-level paved road VKT to obtain link-level paved road dust emissions (GonzalezAyala, 2008a).

2005 Mexicali Emissions Inventory 

### 3-10 Final, 2/27/09

### Figure 3-2. Pavement Coverage of Mexicali Roads

Pavement coverage detail for urban Mexicali

2005 Mexicali Emissions Inventory 

### 3-11 Final, 2/27/09

### 3.1.6 Unpaved Road Dust Unpaved road dust emissions were estimated using the most recent emission factor equation presented in AP-42, Section 13.2.2 (revi sed November 2006) (U.S. EPA, 1995). Silt samples were collected at 10 locations in Mexi cali by ICAR in July 2008. The 10 locations are shown in Figure 3-1 (marked with lette rs A through J) and are as follows:

• A – Calle Caoba between Calle Mauto and Calle Olivo

• B – Calle Mérida between Avenida Nayarit and Avenida Puebla

• C – Avenida Río Atoyac between Calle Onceava and Calle Terceava

• D – Avenida Camino Real between Avenid a Campestre and Avenida Valle Verde

• E – Calle Texcoco between Calzada Laguna de Xochimilco and Avenida Acolman

• F – General Jesus M. Ga rza at Calle Federación

• G – Avenida Ricardo Flores Magón betw een Calle Bernardo Reyes and Calle Filomeno Mata

• H – Calle de las Delicias between Avenid a de los Insurgentes and Avenida Juan Álvarez

• I – Calle Venustiano Carranza between Aven ida Nuevo León and Avenida Querétaro

• J – Carretera Relleno Sanitario (on the road to Hipolito Landfill; location in Figure 3­ 1 is not to scale) All samples were collected according to the sampling procedures presented in Appendix C.1 of AP-42. The samples were analyzed by Labor atorio de Muestreo y Análisis, S.A. de C.V. in Tijuana according to the analytical proce dures presented in Appe ndix C.2 of AP-42 (LMA, 2008b). The unpaved road dust emission factors were calculated using the AP-42 methodology. Three different emission factors were calculate d at each sampling site using different vehicle speeds: urban (11 miles per hour [mph]), suburba n (18 mph), and rural (25 mph). Three average emission factors (i.e., urban, suburban, and rural) we re then calculated by averaging together the emission factors over all of the sites. A precipitation adjust ment factor of 0.94247 corresponding to 21 “wet” days was also used (NWS, 2005).

2005 Mexicali Emissions Inventory 

### 3-12 Final, 2/27/09

### Three different sets of unpaved emission factors (PM 10 

### and PM 2.5 

### ) were estimated for urban, suburban, and rural unpaved roads. The estimated unpaved road dust emission factors were then combined with the link-level unpave d road VKT to obtain link-level unpaved road dust emissions (Gonzalez-Ayala, 2008b). The VKT estimate used for estimating paved road dust emissions was developed in conjunction with the on-road mobile source VKT development (see Section 4.0 of this report). Figure 3-2 demonstrates a portion of the analysis to determine the spl it of paved and unpaved analysis; further details are located in Appendix B, Section VI, of this report.
3.1.7 Miscellaneous Burning Categories Area source inventories can potentially ha ve a number of mi scellaneous burning categories. These include wildfires, prescribed fires, on-site incine ration, open burning, and structural fires. Based on site visits and interviews, the only miscellaneous burning activity that occurs in Mexicali is structural fires. The number of structural fire s was obtained from the Mexicali fire department (Ruiz Burgueño, 2008), while the appropriate emission factors were obtained from EIIP guidance (EIIP, 2001a).
3.1.8 Border Crossings Emissions from idling vehicles queued to ente r the United States were estimated as an area source, and were not include d in the on-road mobile sources estimates. There are three border crossings in Mexicali: Calexico and Calexi co East (both across fr om urban Mexicali) and Andrade (across from Algodones). Vehicle coun t and queue waiting times were obtained from the Bureau of Transportation Statistics (BTS, 2008). Emission fact ors were estimated using the MOBILE6-Mexico emission factor model (ERG, 2003).
3.1.9 Brick Kilns The municipality of Mexicali ha s two distinct brick kiln areas. The first area is in Colonia Campestre and the sec ond area is in Algodones (i.e., northeast Mexicali). Emissions from the Algodones brick kiln area were previously estimated as part of the 2007 inventory work in San Luis Río Colorado and northeast ern Mexicali (ENVIRON and ERG, 2008); these emissions were re-used for the Mexicali inventory. Accurate activity data could not be collected at Colonia Campestre, therefore emission esti mates from the smaller San Luis Río Colorado

2005 Mexicali Emissions Inventory 

### 3-13 Final, 2/27/09

### brick kiln area (i.e., Colonia La Grullita) were assumed to be representative of the Colonia Campestre brick kiln emissions.
3.1.10 Charbroiling, Construction Dust, and Bakeries Detailed Mexicali-specific activity data for ch arbroiling (restaurants and street vendors), construction dust, and bakeries c ould not be obtained. Emissions for these area source categories were extrapolated from the 1999 Mexico NEI (ERG, 2006). Construction dust emissions were disaggregated into two categories: reside ntial constructi on and industrial/commercial construction. 3.1.11 Landfills The municipality of Mexicali has one landfill (H ipolito landfill) which is located south of the urban area. Emissions were estimated using U. S. EPA’s theoretic first order kinetic model of methane production (U.S. EPA, 1995). Many of th e site-specific data needed to make an accurate estimation of emissions (e.g., methane generation potential, methane generation rate constant, temperature, etc.) were not availa ble. The only information that was obtained regarding the landfill was the year it opened and the daily quantity of waste landfilled (Villegas Ibarra, 2008). Default values were used fo r all other necessary parameter variables.
3.1.12 Wastewater Treatment The municipality of Mexicali has eight wastew ater treatment facilities: four in Mexicali and single facilities in Ciudad Morelos (Cuer vos), Guadalupe Victoria (Km. 43), Vicente Guerrero (Algodones), and San Felipe. These fac ilities have a combined installed capacity of 2.964 cubic meters per second (m 3 

### /s) with an actual treatment rate of 1.8494 m 3 

### /s (CNA, 2007). This treatment rate translates to an an nual flow of 58,322,678,400 liters. The VOC emission factor for wastewater treatment was then a pplied to this annual flow (U.S. EPA, 1991).
3.2 Results The 2005 Mexicali area source emissions inve ntory is presented in Table 3-2. Examination of Table 3-2 indicate s the following regarding area s ource emissions in Mexicali in 2005:

• Agricultural field burning is the largest source of NO x 

### followed by industrial natural gas combustion and residential LPG combustion.

2005 Mexicali Emissions Inventory 

### 3-14 Final, 2/27/09Table 3-2. 2005 Mexicali Area Source Emissions (Tons per Year)Source CategoryNOxSO2VOCCOPM10PM2.5NH3CH4Industrial DistillateCombustion66.472.290.670.003.270.820.000.17Industrial ResidualCombustion0.000.001.580.0011.0610.450.005.64Industrial Natural GasCombustion279.790.006.300.008.408.734.45Commercial NaturalGasCombustion2.580.020.142.170.200.200.06Residential Natural GasCombustion7.330.050.433.120.590.590.18Industrial LPGCombustion56.330.163.4332.093.023.020.82Commercial LPGCombustion23.630.071.4413.461.271.270.34Residential LPGCombustion102.970.296.2758.655.525.521.49Paved Road Dust4,609.79422.18Unpaved Road Dust42,331.354,209.66Charbroiling/StreetVendors2.779.69150.1875.4460.21Bakeries117.94Construction Activities– Residential16.773.49Construction Activities– Industrial/Commercial118.5924.65Architectural SurfaceCoating(Solvent-based)311.48Architectural SurfaceCoating (Water-based)167.27AutobodyRefinishing78.74TrafficMarkings57.06Industrial SurfaceCoating - WoodFurniture64.78Industrial SurfaceCoating - Other MetallicProducts1,007.07

2005 Mexicali Emissions Inventory 

### 3-15 Final, 2/27/09Table 3-2. ContinuedSource CategoryNOxSO2VOCCOPM10PM2.5NH3CH4Industrial SurfaceCoating – Electronics32.47Industrial SurfaceCoating – Automotive1,281.22Industrial SurfaceCoating - OtherTransportation327.24Industrial SurfaceCoating - OtherManufacturing1,262.62Industrial SurfaceCoating - IndustrialMaintenance433.48Degreasing (Vapor andIn-line cleaning - Other)323.47Degreasing (Vapor andIn-line cleaning –Electronics284.68Degreasing (ColdCleaning – Other)744.18Degreasing (ColdCleaning - AutomotiveRepair)565.11DryCleaning145.74GraphicArts346.90Consumer Solvents -PersonalCare993.18Consumer Solvents –Household84.62Consumer Solvents -AutomotiveAftermarket67.61Consumer Solvents –Coatings242.98Consumer Solvents -AdhesivesandSealants761.13Consumer Solvents -Federal Insecticide,Fungicide and409.59

2005 Mexicali Emissions Inventory 

### 3-16 Final, 2/27/09Table 3-2. ContinuedSource CategoryNOxSO2VOCCOPM10PM2.5NH3CH4Rodenticide Act(FIFRA) ProductsConsumer Solvents -Miscellaneous29.58AsphaltApplication145.97AgriculturalPesticides216.60Gasoline Distribution –StageI12.72Gasoline Distribution –StageII419.88Gasoline Distribution -Spillage25.45Underground TankBreathing38.17Tank Truck Transit4.45Landfills235.466,019.90WastewaterTreatment835.76Agricultural Tilling929.76206.13Agricultural Harvesting767.74170.21Agricultural FieldBurning621.30126.571,078.0717,384.111,596.171,521.23Fertilizer application –Anhydrousammonia660.75Fertilizer application –Urea4,878.75Fertilizer application –N-P-K436.84Fertilizer application –Calciumnitrate2.69Fertilizer application –Diammonium phosphate1.38Beef cattle feedlots290.72Livestock – Dairy Cattle(NH3and VOC)322.771,389.78Livestock –Beef Cattle(NH3and VOC)1,025.421,589.51Livestock – Swine(NH3and VOC)10.9236.65

2005 Mexicali Emissions Inventory 

### 3-17 Final, 2/27/09Table 3-2. ContinuedSource CategoryNOxSO2VOCCOPM10PM2.5NH3CH4Livestock – Sheep(NH3and VOC)4.4313.63Livestock – Goats (NH3and VOC)2.773.70Structure Fires0.110.854.640.820.760.22Border Crossing20.650.4146.86428.042.281.911.56Brick Kilns21.551.23704.91777.55106.51102.50Total1,205.48131.0915,301.5518,854.0250,879.896,753.659,015.236,033.28Blanks indicate pollutant(s) are not applicable; 0.00 indicates pollutant emissions are insignificant.

2005 Mexicali Emissions Inventory 

### 3-18 Final, 2/27/09

### • Agricultural field burning is also the largest source of SO 2 

### . Typically, there would be high SO 2 

### emissions from industrial residual co mbustion, but reconciliation with the point source inventory reduced the ar ea source industrial residual combustion emissions to zero. In fact, the estimated SO 2 

### emissions from industrial residual combustion were 3,275 tons.

• There are a number of VOC sources that emit approximately 1,000 tons per year: agricultural field burning, several types of industrial surface coating, several types of consumer solvents, wastewater treatment, and dairy cattle. However, if some of these subcategories are aggregated together, th en overall, industria l surface coating and consumer solvents are the most significant area source emitters of VOC.

• Paved and unpaved road dust are the most important PM 10 

### and PM 2.5 

### sources, while agricultural field burning is al so an important source of PM 2.5 

### .

• Fertilizer application and livesto ck ammonia are the predominant NH 3 

### sources, while the landfill is the largest CH 4 

### source. 3.3 Quality Assurance Several general quality assurance checks were conducted for the entire Mexicali inventory. These checks (inc luding hand calculations, comple teness checks against the 1999 Mexico NEI, emission factor checks, etc.) were conducted for all area source categories. In addition, two of the overall five DQOs for the Mexi cali inventory project are applicable to area source emissions:
1. A comprehensive emissions inventory for all source types (i.e., point, area, on-road motor vehicle, and nonroad mobile sources ) and all major categories within these source types. Acceptance criteria: The source types and major categories accounted for in the EIIP guidance documents are present in th e Mexicali emissions inventory, and/or any differences are documented. 2. Area, On-Road Motor Vehicle, and Nonroa d Mobile Sources: Emissions estimates with an accuracy of ±50%. Acceptance criteria: Estimated emissions using emission factors/models compare to emissions estimated using a fuel-balance approach for all applicable pollutants. The fuel-balance approach will use national statistics, while the preferred inventory methodology will use PEMEX and local fuel distribution data. The Mexicali area source i nventory meets DQO #1. The area source inventory categories were based upon the area source cat egories previously inventoried in the 1999 Mexico NEI. The

2005 Mexicali Emissions Inventory 

### 3-19 Final, 2/27/09

### 1999 Mexico NEI area source categories were selected by reviewing all source categories included in Emission Inventory Improvement Pr ogram (EIIP) and other U.S. EPA guidance documents; Mexican federal environmental staff th en identified those cate gories that either did not exist in Mexico or could be considered to be insignificant. Based upon comments to the draft inventory report, one area sour ce category was added (i.e., VOC fr om beef cattle feedlots) and one area source category was disaggreg ated into two (i.e., constructi on activities into residential construction activities an d industrial/commercial c onstruction activities). With regard to DQO #2, most area source fu el combustion emissions could not be assessed using a fuel-balance approach due to the l ack of specific fuel usage data. However, the accuracy of the border crossing emission estimate s was evaluated. Border crossing emissions (i.e., idling emissions) are typically not estimated in the U.S. since the wait times resulting in increased emissions generally occur on the Mexican side of the border. The preferred estimation methodology is to obtain queue waiting times a nd then calculate the distance based upon the lowest allowable vehicle speed in the MOBILE6 emission factor model (i.e., 2.5 miles per hour or 4 kilometers per hour). This calculated distance is then multiplied by the emission factor corresponding to the lowest al lowable vehicle speed (Brzezinski, 2009). This methodology was used to estimate border crossing emissions in Mexicali. All inputs to the model and all subsequent post-model emission calculations were rechecked and no errors were found. Besides the methodology used to estimate border crossing emissions, accurate idling emissions data are relatively una vailable (Brzezinski, 2009). Only one somewhat relevant source of data (i.e., a guidance document related to “long duration idling” for heavy-duty diesel vehicles) was identified (U.S. EPA, 2004). Th e typical hourly idling em ission factors for heavyduty diesel vehicles identified from this guidance document were 135 g NO x 

### /hour, and 3.68 g PM 10 

### /hour and g PM 2.5 

### /hour. The heavy-duty diesel vehi cle emission factors used in the Mexicali emission estimates were 15.96 g NO x 

### /mile, 1.27 g PM 10 

### /mile, and 1.14 g PM 2.5 

### /mile. At 2.5 miles per hour, these emissi on factors translate to 39.9 g NO x 

### /hour, 3.18 g PM 10 

### /hour, and 2.85 g PM 2.5 

### /hour. Although the PM 10 

### and PM 2.5 

### emission factors are comp arable with those in the guidance document, the NO x 

### emission factor differs by a factor of over 3.

2005 Mexicali Emissions Inventory 

### 3-20 Final, 2/27/09

### However, discussions with U.S. EPA staff fa miliar with idling emissions indicated that long duration idling does not correspond with border crossing idling. Long duration idling involves engine idling of long-ha ul heavy-duty diesel trucks during mandated rest periods (i.e., typically overnight or 10 hours rest for every 14 hours of driving) to operate air conditioning, heat, or on-board appliances (e.g., televisions, computers, microwaves , etc.). This type of idling occurs with higher engine loads and is fundamenta lly different than idling that occurs at border crossings (Brzezinski, 2009). In addition, que ue waiting times for the three Mexicali border crossings were considerably s horter than the overnight (or lo ng duration) idling (BTS, 2008). Thus, it is not unexpected that the NO x 

### emissions during long duration idling would be substantially greater than emissions from idling at border crossings. Given these findings, the border crossing emissions were not adjusted.

2005 Mexicali Emissions Inventory 

### 3-21 Final, 2/27/09

# 4.0 ON-ROAD MOBILE SOURCES

On-road mobile sources include all motor vehi cles that are allowe d to operate on public roadways (e.g., cars, trucks, buses, motorcycle s). On-road mobile source emissions include exhaust emissions, as well as various types of evaporative emission processes. Several other types of emissions are caused by on-road mob ile sources, including paved and unpaved road dust, Stage II vehicle refueling, and emissions from vehicles idli ng at the border crossings; these source categories are estimated as area sources and are addressed in Sec tion 3.0 of this report. Based upon previous experience from developi ng the 1999 Mexico NEI, availability of the VKT data needed to estimate emissions fromon-road motor vehicles at the municipalitylevel is very limited in Mexico (with the excepti on of Mexico City, Guadalajara, and other large metropolitan areas). Therefore, traffic and conge stion modeling were used to estimate VKT by road segment (i.e., link) for the municipality of Mexicali. The link-level VKT estimates were combined with emission factors generated by the MOBILE6-Mexico model (i.e., a Mexicospecific version of the MOBILE6 emission factor model) to estimate on-road motor vehicle emissions (ERG, 2003). The methodology is descri bed below, and a more detailed explanation of the methodology is presented in Appendix B.
4.1 Methodology The traffic volume and congestion modeli ng used a simplified road network, which included freeways, main arterials, collector roads, and “connectors” (i.e., artifici al links that modeled local traffic flows). Although the ro ad network was simplified, the road network consisted of 3,603 individual links. In general, the road network is primarily concentrated in the Mexicali urban area, but Federa l Highway 2 (connecting Mexicali to Tijuana and San Luis Río Colorado), Federal Highway 5 (connecting Mexica li to Ensenada and San Felipe), as well as rural roads in the agricultural areas in the northea st and rural areas in th e south, are also modeled. The traffic modeling relied upon known Mexica n trip generation ra tes combined with demographic and socio-economic information (i.e., population, household size, income, and employment) for each AGEB (i.e., Mexican equivale nt of a census tract). The traffic modeling resulted in daily VKT estimates for daily traffi c volumes for each link in the roadway network.

2005 Mexicali Emissions Inventory 

### 4-1 Final, 2/27/09

### Link-level VKT was then estimated by multiplying each link’s daily traffic volume by its length in kilometers. Figure 4-1 shows the Mexicali roadway network and traffic volume intensity during a typical week day. The traffic volumes for individual roadway network links were assigned to corresponding link-specific congested speed emissions factors from MOBILE6-Mexico in order to estimate daily link-level emissions. Se veral sets of emission factors were developed to account for different congested speeds (i.e., from 3 to 65 miles per hour [mph]), va riations in ambient temperature, and seasonality. Overall emissi ons were estimated by summing the link-level emissions over the entire Mexicali road network.
Emissions were estimated for the foll owing seven vehicle classifications:

• Light-Duty Gasoline Vehicles (LDGV)

• Light-Duty Gasoline Trucks (LDGT)

• Heavy-Duty Gasoline Vehicles (HDGV)

• Light-Duty Diesel Vehicles (LDDV)

• Light-Duty Diesel Trucks (LDDT)

• Heavy-Duty Diesel Vehicles (HDDV)

• Motorcycles (MC)
4.2 Results The annual on-road mobile source emissions ar e presented in Table 4-1. Examination of Table 4-1 indicates the following regarding on-ro ad mobile source emissions in Mexicali in 2005:

• Light-duty gas vehicle and trucks ar e the primary sources of VOC, CO, NH 3 

### , and CH 4 

### .

• Heavy-duty diesel vehicles are the major sources of NO x 

### , SO 2 

### , PM 10 

### , and PM 2.5 

### .

• Light-duty diesel vehicles and trucks, as well as motorcycles are relatively insignificant contributors of all pollutants - primarily due to their comparatively small fraction of the overall VKT.

2005 Mexicali Emissions Inventory 

### 4-2 Final, 2/27/09

### Figure 4-1. Mexicali Loaded Roadwa y Network Under Weekday Conditions

Dail y vehicle flow

Detail of roadway loaded network for urban Mexicali

2005 Mexicali Emissions Inventory 

### 4-3 Final, 2/27/09

### Table 4-1. 2005 Mexicali On-Road Mob ile Source Emissions (Tons per Year) NO x 

### SO 2 

### VOC CO PM 10 

### PM 2.5 

### NH 3 

### CH 4 

### LDGV 1,151.9 25.4 4,550.3 29,441.3 143.3 117.9 124.6 58.4 LDGT 1,523.4 33.1 3,594.6 26,650.3 184.1 155.4 113.5 61.7 HDGV 374.8 4.4 393.5 2,475.8 8.8 6.6 3.3 6.6 LDDV 12.1 0.0 9.9 18.7 2.2 2.2 0.0 0.0 LDDT 7.7 0.0 5.5 11.0 1.1 1.1 0.0 0.0 HDDV 5,463.0 105.8 340.6 1,574.1 424.4 382.5 11.0 13.2 MC 37.5 0.0 82.7 432.1 1.1 0.0 0.0 4.4 Total 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 

### 4.3 Quality Assurance
As discussed in Section 1.3, a number of general quality assu rance checks were conducted for the entire Mexica li inventory. These checks (i ncluding hand calculations, completeness checks against the 1999 Mexico NEI, emission factor checks, etc.) were conducted for all on-road mobile source categories. In ad dition, two of the five data quality objectives (DQOs) for the Mexicali inventory project are applicable to on-ro ad mobile source emissions:
1. A comprehensive emissions inventory for all source types (i.e., point, area, on-road motor vehicle, and nonroad mobile sources ) and all major categories within these source types. Acceptance criteria: The source types and major categories accounted for in the EIIP guidance documents are present in th e Mexicali emissions inventory, and/or any differences are documented. 2. Area, On-Road Motor Vehicle, and Nonroa d Mobile Sources: Emissions estimates with an accuracy of ±50%. Acceptance criteria: Estimated emissions using emission factors/models compare to emissions estimated using a fuel-balance approach for all applicable pollutants. The fuel-balance approach will use national statistics, while the preferred inventory methodology will use PEMEX and local fuel distribution data. The Mexicali on-road mobile source i nventory meets DQO #1. Emissions were estimated for seven on-road mobile source cate gories: light-duty gaso line vehicles (LDGV), light-duty gasoline trucks (LDGT), heavy-duty gasoline vehicles (HDGV) , light-duty diesel vehicles (LDDV), light-duty dies el trucks (LDDT), heavy-duty diesel vehicles (HDDV), and motorcycles (MC). The MOBILE6-Mexico emission factor does provide em ission factors for 28

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### disaggregated categories; however, the use of 7 categories is consistent with the 1999 Mexico NEI and provides easier reporting.
Also, the Mexicali on-road mobile source inventory also meets DQO #2. The on-road mobile source quality assurance took two forms: comparison of inventory VMT estimates with fuel-based VMT estimates and comparison of invent ory motor vehicle activity data with Imperial County motor vehicle activity data. The first quality assurance step was to compare the Mexicali VMT estimates derived using traffic and congestion modeling with fu el-based VMT estimates; VMT estimates were used instead of VKT estimates for ease of comp arison. As part of the Mexicali traffic and congestion modeling, weekday VMT was estimat ed to be 7,876,541 miles (Appendix B, Table 11) and weekend day VMT was estimated to be 5,496,411 miles (Appendix B, Table 12). Assuming 52 weeks per year consisting of 5 wee kdays and 2 weekend days, then annual VMT was estimated to be 2.63 × 10 9 

### miles. As described in the area source fuel combustion and evaporative VOC methodology sections of this report (Sections 3.1.1 and 3.1.2), 2005 fuel sales statistics were provided by the PEMEX Mexicali bulk terminal. Additional research conducted as part of this QA procedure indicated that a si gnificant portion of the fuels sold by the PEMEX Mexicali bulk terminal was sold and transporte d for consumption in Sonora (e.g., San Luis Río Colorado, Puerto Peñasco, Sonoyta, etc.) and othe r parts of Baja California (i.e., Tecate and Ensenada). Therefore, PEMEX st aff provided revised fu el sales statistics of the fuel actually sold in the municipa lity of Mexicali (i.e., 224,800,401 liter s of Magna gasoline, 63,773,310 liters of premium gasoline, and 84,678,057 liters of diesel) (Parra Armenta, 2009). Based on a PEMEX assumption that 60 percent of the diesel is used in motor vehicles, there is a total of 89,664,609 gallons of motor vehicle fuel used in Me xicali. The new vehicle fuel economy for Mexico light-duty vehicles and trucks has been estimated to be 20.5 miles per gallon (mpg) (An and Blumberg, 2005); this value is likely overestimated for the ove rall in-use vehicle fleet, but fuel economy statistics for the overall Mexico in-use vehicle fleet c ould not be identified. Multiplying the motor vehicle fuel statistics by the fuel economy resulted in an annual VMT estimate of 1.84 × 10 9 

### miles. The fuel-based VMT estimate is approximately 70 percent of the VMT estimate developed using traffic and conge stion modeling. Given that the Mexicali VMT estimate developed using traffic and congestion m odeling has an approximate uncertainty of ±20

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### percent (Gonzalez-Ayala, 2008c), then the VM T estimate based on traffic and congestion modeling could potentially be overest imated slightly. This is within the 50 percent uncertainty specified by DQO #4. The second quality assurance step was to co mpare Mexicali motor ve hicle activity data with Imperial County motor vehicle activity data for reasonableness based upon comments regarding the draft report. Overall Imperial County and Mexicali on-road mobile source emissions data are compared in Table 4-2. Table 4-2. 2005 Imperial County and Mexicali On-Road Mobile Source Emissions (Tons per Year) Imperial County (tons/day) Imperial County (tons/year) Mexicali (tons/year) Ratio (Mexicali/Imperial) NO x 

### 19.59 7,150.4 8,570.4 1.199 SO 2 

### 0.14 51.1 168.7 3.301 VOC 6.41 2,339.7 8,977.1 3.837 CO 56.52 20,629.8 60,603.4 2.938 PM 10 

### 0.86 313.9 765.0 2.437 PM 2.5 

### 0.71 259.2 665.8 2.569 

### Imperial County emissions data were obtai ned from ARB’s Almanac Emission Projection Data (ARB, 2007). As can be seen in the ratios in Table 4-2, the Mexicali on-road mobile source emissions exceed the Imperial County emissions by a factor of 1.2 to 3.8.
As described in the area source met hodology (Section 3.0 and Appendix A), the 2005 population of the municipality of Mexical i was 855,962 people. The 2005 population of Imperial County was estimated to be 153,857 peopl e (U.S. Census, 2008). The ratio of Mexicali population to Imperial County population is 5.563. If on-road mobile source emissions were proportional to population, then the Mexicali on-ro ad mobile source emissions would exceed the Imperial County emissions by a similar factor of 5.563. One potential reason why the Mexicali/Imper ial County emission ratios are lower (i.e., 1.2 to 3.8) than the population ratio (i.e., 5.563) is that there is a significantly higher level of vehicle ownership in Imperial County compared to the municipality of Mexicali. Chapter 4 of the Imperial County Air Quality Manageme nt Plan (AQMP) provides a count of 121,707 vehicles for the year 2002 and an estimate of 161,231 vehicles for the year 2009 (Imperial, 2008); linear interpolation between these two da ta points resulted in an estimate of 138,646

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### vehicles for the year 2005. This results in an estimated vehicle ownership rate of 0.901 vehicles per person. Vehicle registration statistics for the municipality of Mexicali obtained from INEGI indicate a total of 422,35 7 vehicles registered in 2005 (INEG I, 2008), which corresponds to an estimated vehicle owners hip rate of 0.493 vehicles per pers on. Therefore, Imperial County vehicle ownership rates ar e significantly higher than in Mexicali. Chapter 4 of the Imperial County AQMP al so provide estimated daily VMT for 2002 and 2009 (4.780 × 10 6 

### miles and 6.277 × 10 6 

### miles, respectively) (Imperial, 2008); linear interpolation between these two data points re sulted in an estimate of 5,421,571 daily VMT in 2005, which corresponds to an annual VMT estimate of 1.98 × 10 9 

### miles. Overall Mexicali VMT is higher than overall Imperial County VMT by a factor of 1.33. Perceptions of even higher levels of vehicle activity in Mexicali compared to Imperial County are potentially due to Mexicali vehicle activity being c oncentrated in the urban porti on of Mexicali, compared to Imperial County vehicle activity being spread among a number commun ities located throughout the county (e.g., El Centro, Brawley, Holtv ille, Calipatria, Westmorland, etc.). Based upon these QA analyses, it was felt that the Mexicali on-road mobile source emission estimates were reasonably accurate and that additional adjustments were not needed.

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# 5.0 NONROAD MOBILE SOURCES
Nonroad mobile sources include all equipment that move under their own power, or that are portable, and are not allowed to operate on public roadways. For the 2005 Mexicali emissions inventory, nonroad mobile sources include aircraft and airport ground support equipment and auxiliary power units, locomotives, and agricultural and cons truction equipment. Another nonroad source type is commercial marine vessels; however, no major ports with significant commercial ma rine vessel activity exist in Mexica li. Initially, it was thought that there might be some fishing vessels in some of the communities located on the Gulf of California. However, fishing vesse l activity data were not available. Some fishing occurs out of San Felipe, but this appears to co nsist of tourist fishi ng, rather than commercial fishing. Due to the lack of data and apparent insignificance of these emissions, emissions for commercial marine vessels were not estimated. 5.1 Methodology The methodologies used to estimate emissi ons for the various nonroad mobile source types operating in Mexicali are described below.
5.1.1 Aircraft There are two airports located in the municipality of Mexica li: General Rodolfo Sánchez Taboada International Airport (east of the Me xicali urban area) and Sa n Felipe International Airport (located in the southern portion of the municipality). Operator-specific landing and takeoff (LTO) statistics and ai rcraft fleet composition data we re obtained from local airport officials (Sanchez Resendiz, 2008). These data included information for both the Mexicali and San Felipe airports; however, the data were not associ ated with a particular airport. Therefore, it was assumed that all flight data were associ ated with the General Rodolfo Sánchez Taboada International Airport airport because it is much larger than San Felipe International Airport. Because of the large amount of agricultural activit y in Mexicali, crop dusting (or aerial spraying) is a significant activity in the area. Crop dus ting activity data (incl uding LTO data, flight lengths, and fuel usage) were provided by staff from the Mexicali crop dusting industry association (Tamayo Moreno, 2008). There were also numerous other general aviation landing

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### strips located throughout Baja California, but th ese were not included in the inventory because LTO data for these landing st rips were not available. The aircraft operations informa tion was input into the latest version of the Emissions and Dispersion Modeling System (EDMS 5.0.2, releas e June 29, 2007) model. The EDMS model was developed by the Federal Aviation Administ ration and is recommended for use by U.S. EPA. The model contains the latest emission factors from the International Civil Aviation Organization’s (ICAO) Engine E xhaust Emissions Data Bank. The default time-in modes for taxi out, take off, climb out, a pproach, and taxi in were used to run the model. The model output included emissions by aircraft t ype and total emissions, including emissions for aircraft, ground support equipment, and auxiliary power units. Em issions from other airp ort-related activities (e.g., parking lots, roadways, st ationary sources within airp ort boundaries, etc.) were not included in the estimates because these source s addressed within ot her emission inventory categories.
5.1.2 Locomotives A railway line extends from Mexicali to Puerto Peñasco, Sonora. In order to estimate emissions for locomotives operating within the m unicipality of Mexicali (i.e., in the locomotive yard and from line haul activitie s), local locomotive fuel consumption data are needed; however, these data were not available. Visual observations ERG staff indicated that there is very limited locomotive activity in the municipality. Due to the lack of data needed to estimate these emissions for Mexicali, the emissions calculate d for the 1999 Mexico NEI were used without adjustment. These are felt to be appropriate beca use of the observed limited locomotive activity.
5.1.3 Agricultural and Construction Equipment As part of the 1999 Mexico NEI project, a Me xico-specific version of the U.S. EPA’s NONROAD emissions model was developed (i .e., NONROAD-Mexico), which provided a streamlined way to estimate emissions from agricultural and construction equipment (ERG, 2005). To develop a Mexico-specific version of this model, various parameters were adjusted to better reflect conditions in Mexi co, including: average annual temperature, al titude, and diesel sulfur content. Also, Mexican state-level ag ricultural and construction equipment populations were incorporated into the model. Although ot her nonroad equipment types (lawn and garden,

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### logging, etc.) exist in Mexico, agricultural a nd construction equipment were given highest priority during the development of the NONROAD- Mexico model because it was believed that they emitted the most emissions as compared to the other equipment types. Because the NONROAD-Mexico model contained data which would generate emissions for 1999, only, it was desirable to update the equi pment population and oper ation files to 2005 in order to estimate 2005 emissions for the municipali ty of Mexicali. However, sufficient recent data were not available. Due to the lack of data needed to update the NONROAD-Mexico model, the municipality-level nonroad equipment emissions from the 1999 Mexico NEI generated using the NONROAD-Mexico model were extrapolated using 2005 population for Mexicali. This extrapolation method was previously used to es timate nonroad source emissions in the San Luis Río Colorado emission inventory, which included the northeast portion of the municipality of Mexicali (ENVIRON and ERG, 2008) . First, for agricultural equipment, it was assumed that agricultural land area would be fairly stable over time. As a result, the agricultural equipment population would likely not gr ow significantly between 1999 and 2005. For construction equipment, it was assumed that equipment popul ation growth was propor tional to population growth. The population of the municipality of Mexicali grew from 764,602 to 855,962 over the same time period; this corresponds to a growth rate of 1.1195 (INEGI, 2000; INEGI, 2006). This growth factor was applied to the 1999 m unicipality-level emissions estimated using NONROAD-Mexico to obtain 2005 em issions for agricultural a nd construction equipment. 5.2 Results The annual nonroad mobile source emissi ons are presented in Table 5-1; NH 3 

### and CH 4 

### emissions were not estimated for nonroad mobile sources. As seen in the table, the largest emissions are from construction and agricultural equipment. The construction and agricultural equipment are dominated by diesel-powered equipment which will cause relatively high NO x 

### , PM 10 

### , and PM 2.5 

### emissions. Locomotives have infrequent activity and the portion of aircraft flight durations that occur in the inventory domai n are comparatively short relative to the length of an entire flight, with the exception of cropdusters.

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### Table 5-1. 2005 Mexicali Nonroad Mob ile Source Emissions (Tons per Year) Source Category NO x 

### SO 2 

### VOC CO PM 10 

### PM 2.5 

### Aircraft – Commercial 15.2 1.9 6.8 31.7 0.3 0.3 Airport Auxiliary Power Units 3.7 0.3 1.1 32.4 0.1 0.1 Airport Ground Support Equipment 0.6 0.1 0.0 0.4 0.0 0.0 Locomotives 206.2 1.8 7.8 20.4 5.1 4.6 Construction Equipment 3,100.2 41.6 320.4 1,552.3 348.9 338.5 Agricultural Equipment 1,158.5 14.6 195.7 785.3 195.7 189.8 Aircraft – Cropdusters (General) 0.5 0.6 11.1 784.4 0.0 0.0 Total 4,485.0 60.9 542.9 3,206.9 550.2 533.3



### 5.3 Quality Assurance As discussed in Section 1.3, a number of general quality assu rance checks were conducted for the entire Mexicali inventory. These checks (including hand calculations, completeness checks against the 1999 Mexico NEI, emission factor checks, etc.) were conducted for all area source categories. In addition, two of the five DQOs for the Mexicali inventory project are applicab le to nonroad mobile source emissions:
1. A comprehensive emissions inventory for all source types (i.e., point, area, on-road motor vehicle, and nonroad mobile sources ) and all major categories within these source types. Acceptance criteria: The source types and major categories accounted for in the EIIP guidance documents are present in th e Mexicali emissions inventory, and/or any differences are documented. 2. Area, On-Road Motor Vehicle, and Nonroa d Mobile Sources: Emissions estimates with an accuracy of ±50%. Acceptance criteria: Estimated emissions using emission factors/models compare to emissions estimated using a fuel-balance approach for all applicable pollutants. The fuel-balance approach will use national statistics, while the preferred inventory methodology will use PEMEX and local fuel distribution data. The Mexicali nonroad mobile source inve ntory meets DQO #1. The nonroad mobile source inventory categories were based upon the n onroad mobile source categories previously inventoried in the 1999 Mexico NEI. The 1999 Mexico NEI nonroad mobile source categories were selected by reviewing all source categor ies included in Emission Inventory Improvement Program (EIIP) and other U.S. EPA guidance docu ments; Mexican federal environmental staff

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### then identified those categories that either did not exist in Mexico or could be considered to be insignificant. Some U.S.-specific nonroad mob ile source categories typically inventoried were excluded in Mexico (e.g., lawn and garden equi pment, logging equipment, recreational vehicles, etc.). It should also be noted that the aircra ft and locomotive categories were included as area sources in the 1999 Mexico NEI, but were classi fied as nonroad mobile sources for the Mexicali inventory. Also, two nonroad mob ile source categories not prev iously inventoried in the 1999 Mexico NEI were included in the Mexico inve ntory (i.e., airport aux iliary power units and airport ground support equipment). Finally, base d upon comments to the draft inventory report, one additional nonroad mobile source cate gory was added (i.e., cropdusting aircraft). With regard to DQO #2, there were insuffici ent fuel usage data to assess the nonroad mobile source emissions using a fuel balance approach.

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# 6.0 EMISSION INVENTORY RESULTS
The overall 2005 Mexicali emissi ons inventory results are shown in Table 6-1. The results are presented by source ty pe and pollutant. Figures 6-1 through 6-8 show the distribution of emissions by source type for each polluta nt. The table and figures show that:

• Emissions from federal jurisdiction point sources are considerably larger than emissions from state jurisdiction point s ources, even though the ratio of state-tofederal jurisdiction point sour ces is greater than 10-to-1.

• NO x 

### and SO 2 

### emissions are primarily emitted by point sources.

• VOC emissions are emitted by area (i.e., mainly various evaporative source categories) and on-road mobile sources.

• On-road mobile sources are the predominant source of CO.

• Area sources are the largest sources of PM 10 

### and PM 2.5 

### (i.e., due to unpaved and paved road dust), as well as NH 3 

### (i.e., from livestock ammonia and fertilizer application) and CH 4 

### (i.e., from the landfill).
Table 6-1. 2005 Mexicali Emissions Inventory Summary (Tons per Year) Source Type NO x 

### SO 2 

### VOC CO PM 10 

### PM 2.5 

### NH 3 

### CH 4 

### Point – Federal 13,931.7 3,650.9 659.3 3,636.2 1,537.7 128.4 Point – State 444.0 973.2 73.0 479.9 89.7 Area 1,205.5 131.1 15,301.5 18,854.0 50,879.3 6,753.5 9,015.2 6,033.3 On-Road Mobile 8,570.4 168.7 8,977.1 60,603.4 765.0 665.8 252.4 144.4 Nonroad Mobile 4,485.0 60.9 542.9 3,206.9 550.2 533.3 0.0 1.5 Total 28,636.5 4,984.8 25,553.8 86,780.4 53,821.8 8,081.0 9,267.6 6,179.2 Blanks indicate that pollutant emissions were not applicable or were not estimated due to a lack of data.

2005 Mexicali Emissions Inventory 

### 6-1 Final, 2/27/09- 

### Figure 6-1. Mexicali NO x 

### Emissions

Non-Road Mobile

Point - Federal 48.7%

Point - State 1.6% On-Road Mobile 29.9% 15.7%

Area 4.2%



### Figure 6-2. Mexicali SO 2 

### Emissions

- Non-Road Mobile 1.2% On-Road Mobile 3.4% Area 2.6%

Point - State 19.5%

Point - Federal 73.2%

2005 Mexicali Emissions Inventory 

### 6-2 Final, 2/27/09- 

### Figure 6-3. Mexicali VOC Emissions

Point - Federal 2.6% Point - State 2.1% Non-Road Mobile 0.3%

On-Road Mobile 35.1%

Area 59.9%

- 

### Figure 6-4. Mexicali CO Emissions

Point - Federal 4.2% Point - State 3.7% Non-Road Mobile 0.6%

Area 21.7%

On-Road Mobile 69.8%

2005 Mexicali Emissions Inventory 

### 6-3 Final, 2/27/09- 

### Figure 6-5. Mexicali PM 10 

### Emissions

Point - Federal Non-Road Mobile 2.9% 1.0%
On-Road Mobile Point - State 1.4% 0.2%

Area 94.5%

- 

### Figure 6-6. Mexicali PM 2.5 

### Emissions

Point - Federal 1.6% Point - State Non-Road Mobile 0.0% 6.6% On-Road Mobile 8.2%

Area 83.6%

2005 Mexicali Emissions Inventory 

### 6-4 Final, 2/27/09- 

### Figure 6-7. Mexicali NH 3 

### Emissions

Non-Road Mobile Point - Federal 0.0% 0.0% Point - State On-Road Mobile 0.0% 2.7%

Area 97.3%

- 

### Figure 6-8. Mexicali CH 4 

### Emissions

Point - Federal Non-Road Mobile 0.0% 0.0% Point - State On-Road Mobile 0.0% 2.3%

Area 97.6%

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# 7.0 REFERENCES Álvarez Sánchez, 2008. LPG statis tics provided by I. Álvarez Sánc hez, Gas Silza S.A. de C.V., Mexicali, Baja California. August 22. An and Blumberg, 2005. “The Case for Mexico to Improve Vehicle Fuel Efficiency”, presentation by F. An and K. Blumberg at Workshop on Sustainable Transport in Latin America: From Theory to Practice . Washington D.C., January 9. ANAFAPYT, 2003. National paint sa les provided by the National Association of Paint and Ink Manufacturers (Asociación Nacional de Fa bricantes de Pinturas y Tintas). ANAFAPYT, 2004. National ink sale s provided by the National A ssociation of Paint and Ink Manufacturers (Asociación Nacional de Fa bricantes de Pinturas y Tintas). ARB, 2002. California Emission Inventory Developm ent and Reporting System (CEIDARS) Particulate Matter (PM) Speciation Profiles . Updated September 26, 2002. California Air Resources Board, Sacramento, California. ARB, 2003a. Area Sources Methods – Section 7. 4 (Agricultural Land Preparation) . California Air Resources Board, Sacramento, California. ARB, 2003b. Area Sources Methods – Section 7.5 (Agricultural Harvest Operations)

. California Air Resources Board, Sacramento, California. ARB, 2004. Area Sources Methods – Section 7.6 (Livestock Husbandry) . California Air Resources Board, Sacramento, California. May. ARB, 2005. Area Sources Methods – Section 7.17 (A gricultural Burning and Other Burning Methodology) . California Air Resources Board, Sacramento, California. June. ARB, 2007. Almanac Emission Projection Data . California Air Resour ces Board, Sacramento, California. Brzezinski, 2009. Personal communication with D. Brzezinski, U.S. Environmental Protection Agency, National Vehicle and Fuel Emissions Laboratory, Ann Arbor, Michigan. January 13. BTS, 2007. National Transportation Statistics 2007 (Table 1-32 – U.S. Vehicle-Miles) . Bureau of Transportation Statistics, U.S. De partment of Transportation. BTS, 2008. Border crossing vehicle counts and qu eue waiting times provided by the Bureau of Transportation Statistics. CANALAVA, 2002. National dry clean ing solvent statistics provi ded by the National Chamber of the Dry Cleaning Industry (Cámara Naci onal de la Industria de Lavanderías).

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### Chinkin, L.R., P.A. Ryan, and D.L. Coe. 2003. “Recommended Improvements to the CMU Ammonia Emission Inventory Model for Use by LADC O”. Prepared for the Lake Michigan Air Directors Consortium. CNA, 2007. Inventario Nacional de Plantas Municipales de Potabiliza ción y de Tratamiento de Aguas Residuales en Oper ación – Diciembre de 2007 . Prepared by the National Commission on Water (Comisión Nacional del Agua). COFEPRIS, 2007. Registros de Plaguicidas Autoriz ados por Categoría Toxicológica. Prepared by the Federal Commission for Sanitary Risk Prot ection (Comisión Federal de Protección Contra Riesgos Sanitarios). DPR, 2007. Pesticide databases. Californ ia Department of Pesticide Regulation, http://www.cdpr.ca.gov/dprdatabase.htm EEA, 2002. Joint EMEP/CORINAIR Atmospheric Emission Inventory Guidebook, Third Edition . European Environment Agency, Copenhagen, Denmark EIIP, 1996. Consumer and Commercial Solvent Use . Volume III, Chapter 5. Emission Inventory Improvement Program. August. EIIP, 1997. Solvent Cleaning , Volume III: Chapter 6. Emission Inventory Improvement Program, Area Sources Committee. September. EIIP, 2001a. Gasoline Marketing (Stage I and Stage II). Volume III. Chapter 11. Emission Inventory Improvement Program, Ar ea Sources Committee. January. EIIP, 2001b. Pesticides – Agricultural and Nonagricultural . Volume III, Chapter 9. Emission Inventory Improvement Program, Area Sources Committee. June. EIIP, 2001c. Structure Fires . Volume III, Chapter 18. Emission Inventory Improvement Program. January. EIIP, 2001d. Asphalt Paving . Volume III, Chapter 17. Emission Inventory Improvement Program. April. ENVIRON and ERG, 2007. Development of Air Emissions In ventory for the Western Arizona Sonora Border Air Quality Study (WASBAQS) – Emission Inventory Project Report . Prepared for the Arizona Department of Environmental Quality by ENVIRON International Corporation and Eastern Research Group, Inc., June. ERG, 2003. MOBILE6-Mexico . Prepared for the Western G overnors’ Association by Eastern Research Group, Inc. (ERG), Austin, Texas. June 27. ERG, 2005. NONROAD-Mexico: A Tool for Estimati ng Emissions from Agricultural and Construction Equipment in Mexico. Prepared for the Western G overnors’ Association and the Binational Advisory Committee. Prepared by Easter n Research Group, Inc., Au stin, Texas. July 27.

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### ERG, 2006. Mexico National Emissions Inventory, 1999 – Final . Prepared for the Secretariat of the Environment and Natural Resources and th e National Institute of Ecology of Mexico by Eastern Research Group, Inc. with sponsorship of the U.S. EPA, Western Governors’ Association, and the North American Commission for E nvironmental Cooperation. October 11, 2006. Escalante, 2008. LPG statistics pr ovided by E. Escalante, Kino Gas del Noroeste, S.A. de C.V., Mexicali, Baja California. August 7. Félix Robles, 2008. LPG statistics provided by C.R. Félix Robles , Hidrocarburos del Noroeste, S.A. de C.V., Mexicali, Ba ja California. August 13. Fierro, 2007. Personal communication with Marian Fierro (Arizona College of Public Health, University of Arizona, Tucson, Arizona) regarding bleach use in SLRC. May 4. Fierro, M.A. and M.K. O’Rourke, 2007. Binational Household Product Survey . Prepared by Marian A. Fierro and Mary Kay O’Rourke, Colle ge of Public Health, University of Arizona, Tucson, Arizona. Gonzalez-Ayala, 2007. Per capita San Luis Rí o Colorado, Sonora VMT estimates provided by S. Gonzalez-Ayala, TransEngineering, Ciudad Juárez, Chihuahua. December. Gonzalez-Ayala, 2008a. Link-level paved road dust emission estimates provided by S. Gonzalez-Ayala, TransEngineering, Ciuda d Juárez, Chihuahua. September. Gonzalez-Ayala, 2008b. Link-level unpaved road dust emission estimates provided by S. Gonzalez-Ayala, TransEngineering, Ciuda d Juárez, Chihuahua. September. Gonzalez-Ayala, 2008c. Estimates of Mexicali travel demand modeling uncertainty provided by S. Gonzalez-Ayala, TransEngineering, Ciudad Juárez, Chihuahua. December 22. Imperial, 2008. Imperial County Air Quality Manage ment Plan, Chapter 4 – Emissions Inventory . Imperial County Air Pollution Control Di strict, El Centro, California. April 9. INEGI, 2000. XII Censo General de Población y Vivienda, 2000 . Prepared by the National Institute of Statistics, Geography, and Information (Instituto Nacional de Es tadística, Geografía e Informática). INEGI, 2005. Censos Económicos 2004 medianto el Si stema Automatizado de Información Censal (SAIC), version 5.0. Prepared by the National Institute of Statistics, Geography, and Information (Instituto Nacional de Estadística, Geografía e Informática). INEGI, 2006. II Conteo de Población y Vivienda, 2005. Prepared by the National Institute of Statistics, Geography, and Information (Institu to Nacional de Estadí stica, Geografía e Informática). INEGI, 2008. Vehículos de Motor Regist rados en Circulación. Prepared by the National Institute of Statistics, Geography, and Information (Instituto Nacional de Es tadística, Geografía e Informática).

2005 Mexicali Emissions Inventory 

### 7-3 Final, 2/27/09

### LMA, 2008a. Laboratory analysis results of Mexicali paved road dust samples provided by Laboratorio de Muestro y Analisis, S.A. de C.V., Tijuana, Baja California. July 24. LMA, 2008b. Laboratory analysis results of Mexicali unpaved road dust samples provided by Laboratorio de Muestro y Analisis, S.A. de C.V., Tijuana, Baja California. July 24. López Bojorques, 2008. Personal communication with G. López Bojorques, Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pe sca y Alimentación (SAGARPA), Mexicali, Baja California. August 13. Martinez, 2007. Personal communication with K. Martinez, Urban Development and Ecology Office, San Luis Río Colorado, Sonora. January. Miranda Palazuelos, 2008. Residual fuel oil st atistics provided by F.H. Miranda Palazuelos, PEMEX – Rosarito Terminal, Rosarito, Baja California. October 2. NWS, 2005. “Record of River and Climatologica l Observations – Calexico 2NE ”, January 2005 through December 2005. U.S. Weather Service. Parra Armenta, 2008. Gasoline and distillate fuel oil statistics provided by F. Parra Armenta, PEMEX – Mexicali Terminal, Mexical i, Baja California. May 19. Parra Armenta, 2009. Revised gasoline and distil late fuel oil statistics provided by F. Parra Armenta, PEMEX – Mexicali Terminal, Mexicali, Baja California. January 6. PROAIRE, 1999. Programa para Mejorar la Calidad del Aire de Mexicali, 2000-2005

. Prepared by the Gobierno del Estado de Baja California, Gobierno Municipality de Mexicali, Secretaría de Medio Ambiente, Recursos Naturale s y Pesca, Secretaría de Salud, and Delegación Federal SEMARNAP, Baja California. December. Reynoso Ambriz, 2008. Personal communication w ith F. Reynoso Ambriz, Obras Públicas del Ayuntamiento de Mexicali, Mexicali, Baja California. August 13. Rodríguez Hernández, 2008. Agricultural bur ning information provided by A. Rodríguez Hernández, Secretario de Fomento Agropecu ario, Mexicali, Baja California. May 19. Ruiz Burgueño, 2008. Fire statis tics provided by M.A. Ruiz Bu rgueño, Dirección del Heroico Cuerpo de Bomberos, Mexicali, Baja California. May 6. Ruiz Méndez, 2003. PEMEX municipality-level gasoline station information provided by A. Ruiz Méndez, PEMEX, Mexico City, Mexico. July 30. SAGARPA, 2007a. Anuario Estadístico de la Produccíon Agrícola. Prepared by the Secretariat of Agriculture, Livestock, Rural Development, Fi sheries, and Food (Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación). SAGARPA, 2007b. Vínculo Informático para la Obtención de Costos de Producción (VIOCS), Estructuras de Costos de Producción (ECP), Versión 2.0. Prepared by the Secretariat of

2005 Mexicali Emissions Inventory 

### 7-4 Final, 2/27/09

### Agriculture, Livestock, Rural Development, Fi sheries, and Food (Secret aría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación). SAGARPA, 2007c. Servicio de Información Agroalimentaria y Pesquera (SIAP) . Prepared by the Secretariat of Agriculture, Livestock, Rural Development, Fisheries, and Food (Secretaría de Agricultura, Ganadería, Desarroll o Rural, Pesca y Alimentación). Sanchez Resendiz, 2008. Mexicali airport landi ng-and-takeoff (LTO) st atistics provided by C. Sanchez Resendiz, Direcci ón de Aeropuerto. May 15. Sukarne, 2008. Beef cattle residence time estim ates provided by Sukarne Produccion, S.A. de C.V., Mexicali, Baja California. September. Tamayo Moreno, 2008. Crop dusting activity data provided by A. Tamayo Moreno, Asociación de Pilotos Privados Aplicadores de Plaguicida. October 31. U.S. Census Bureau, 2007. Annual Estimates of the Population for the United States , Population Division, U.S. Census Bureau, Washington, D.C. U.S. Census Bureau, 2008. Annual Estimates of Population C hange for Counties of California and County Rankings: April 1, 2000 to July 1, 2007 . Population Division, U.S. Census Bureau, Washington, D.C. U.S. EPA, 1991. Procedures for the Preparation of Emi ssion Inventories for Carbon Monoxide and Precursors of Ozone. Volume I: General Guidance for Stationary Sources . EPA-450/4/91­ 016. U.S. Environmental Protection Agency, O ffice of Air Quality Planning and Standards, Research Triangle Park, North Carolina. U.S. EPA, 1995. Compilation of Air Pollution Emission Factors (AP-42) – Volume I: Stationary Point and Area Sources , Fifth Edition. Various sec tions. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina. January. U.S. EPA, 2004. Guidance for Quantifying and Using Long Duration Truck Idling Emission Reductions in State Implementation Plans and Transportation Conformity . EPA420-B-04-001. U.S. Environmental Protection Agency, Office of Transportation and Air Quality. January. Valdes Cantu, 2003. PEMEX fuel sulfur contents (diesel, combustoleo, marine diesel, and Intermedio 15) provided by N. Valdes Cantu, PEMEX, Mexico City, Mexico. December 11. Villegas Ibarra, 2008. Landfill statistics provided by E. Villegas Ib arra, Dirección de Ecología de Mexicali. May 14. Vivo, 2008. Natural gas statistics provided by A. R. Vivo, Ecogas Mexico S. de R.L. de C.V., Mexicali, Baja California. August 26.

2005 Mexicali Emissions Inventory 

### 7-5 Final, 2/27/09

# APPENDIX A: AREA SOURCE CATEGORY FORMSSOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Distillate

DESCRIPTION: Industrial consumption of distillate fuel. Emission sources in clude boilers, furnaces, heaters, internal combustion (IC) engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 , and CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• Regional-level total distillate fuel usage (Parra Armenta, 2008; Parra Armenta, 2009)
EMISSION FACTORS:
• NO x – 24 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • SO x– 5.966 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • VOC – 0.2 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • CO – 5.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • PM 10 – 1.0 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • PM 2.5 – 0.25 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • NH 3 – 0.052 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) NOTES AND ASSUMPTIONS:
• It was assumed that 30% of the total distillate was industrial distillate. • Bulk terminal-weighted average sulfur content of distillate fuel was calculated to be 0.038% (Valdes Cantu, 2003). • Industrial area source distillate emissions were reconciled with the industrial point source inventory by subtracting point source inventory distillate emissions fr om the area source distillate emissions.
SAMPLE CALCULATIONS: Estimate annual emissions from distillate fuel oil combustion in the industrial sector in Mexicali. Total distillate use (Mexicali terminal) = 84,678,057 liters/yr Industrial fraction = 0.3 (assumed) Total industrial distillate use = 84, 678,057 liters/yr x 0.3 = 25,403,41 7 liters/yr = 6,711,603 gallons/yr Annual municipality-level emissions (reconciled with area sources) : NO xemissions = 24 lbs/1,000 gallons × (6,711,603 gallons) = 80.5 tons – 14. 1 tons = 66.5 tons SO xemissions = 5.966 lbs/1,000 gallo ns × (6,711,603 gallons) = 20.0 t ons – 17.7 tons = 2.3 tons VOC emissions = 0.2 lbs/1,000 gallons × (6,711,603 gallons) = 0.7 tons – 0.0 tons = 0.7 tons CO emissions = 5.0 lbs/1,000 gallons × (6,711,603 gallons) = 16.8 tons – 75.0 tons = -58.2 tons (set to zero) PM 10 emissions = 1.0 lbs/1,000 gallons × (6,711,603 gallons) = 3.4 tons – 0.1 tons = 3.3 tons PM 2.5 emissions = 0.25 lbs/1,000 gallo ns × (6,711,603 gallons) = 0.8 tons – 0.0 tons = 0.8 tons CH 4 emissions = 0.052 lbs/1,000 gallons × (6,711,603 gallons) = 0.2 tons – 0.0 tons = 0.2 tons



### A-1SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Residual

DESCRIPTION: Industrial consumption of residual fuel. Emission sources include boilers, furnaces, heaters, internal combustion (IC) engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 , and CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• Regional-level residual fuel usage (Miranda Palazuelos, 2008)
EMISSION FACTORS:
• NO x – 47 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • SO x– 580.74 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) • VOC – 0.28 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • CO – 5.0 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • PM 10 – 32.36 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • PM 2.5 – 21.08 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.3 – Updated September 1998]) • CH 4 – 1.0 lbs/1,000 gallons (U.S. EPA, 1995 [S ection 1.3 – Updated September 1998]) NOTES AND ASSUMPTIONS:
• Bulk terminal-weighted average sulfur content of residual fuel was estimated to be 3.699% (combustóleo, Intermedio 15, and Industrial Combustible) (Valdes Cantu, 2003). • Industrial area source residual emissions were reconciled with the industrial point source in ventory by subtracting point source inventory residual emissions from the area source residual emissions.

SAMPLE CALCULATIONS: Estimate annual emissions from residual fuel oil combustion in the industrial sector in Mexicali. Regional residual use (Rosarito terminal) = 121,974,708 liters/yr = 32,225,815 gallons/yr Mexicali fraction = 0.35 (assumed) Total industrial distillate use = 32,225, 815 gallons/yr x 0.35 = 11,279,035 gallons/yr Annual municipality-level emissions (reconciled with area sources) : NO xemissions = 47 lbs/1,000 gallons × ( 11,279,035 gallons) = 265.1 tons – 435.6 tons = -170. 5 tons (set to zero) SO xemissions = 580.74 lbs/1,000 gallons × (11,279,035 gallons) = 3, 275.1 tons – 3,776.2 tons = -501.1 tons (set to zero) VOC emissions = 0.28 lbs/1,000 gallons × (11,279,035 gallons) = 1.6 tons – 0.0 tons = 1.6 tons CO emissions = 5.0 lbs/1,000 gallons × (11,279,035 gallons) = 28.2 tons – 31. 9 tons = -3.7 tons (set to zero) PM 10 emissions = 32.36 lbs/1,000 gallons × (11,279,035 gallons) = 182.5 t ons – 171.4 tons = 11.1 tons PM 2.5 emissions = 21.08 lbs/1,000 gallons × (11,279,035 gallons) = 118.9 t ons – 108.4 tons = 10.5 tons CH 4 emissions = 1.0 lbs/1,000 gall ons × (11,279,035 gallons) = 5.6 tons – 0.0 tons = 5.6 tons



### A-2SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – Natural Gas

DESCRIPTION: Industrial combustion of natural gas. Emission sources include boilers, furnaces, heaters, IC engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 , CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data ACTIVITY DATA:
• Annual natural gas quantity used in t he industrial sector (Vivo, 2008) EMISSION FACTORS:
• NO x– 280 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • SO x– 0.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • VOC – 5.5 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CO – 84 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 10 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 2.5 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CH 4 – 2.3 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998])

NOTES AND ASSUMPTIONS:
• Industrial area source natural gas emissions were reconciled with the industrial point source inventory by subtracting point source inventory natural gas emissions from the area source natural gas emissions.
SAMPLE CALCULATIONS: Estimate annual emissions from indust rial natural gas usage in Mexicali. Annual industrial natural gas usage = 109.6 x 10 6 m 3 = (3,871.7 x 10 6 scf) Annual municipality-level emissions – Mexicali : NO x– (3,871.7 x 10 6 scf) x 280 lb/10 6 scf = 542.0 tons – 262.2 tons = 279.8 tons SO x– (3,871.7 x 10 6 scf) x 0.6 lb/10 6 scf = 1.2 tons – 1.9 tons = -0.7 tons (set to zero) VOC – (3,871.7 x 10 6 scf) x 5.5 lb/10 6 scf = 10.6 tons – 4.3 tons = 6.3 tons CO – (3,871.7 x 10 6 scf) x 84 lb/10 6 scf = 162.6 tons – 216.8 tons = -54.2 tons (set to zero) PM 10 – (3,871.7 x 10 6 scf) x 7.6 lb/10 6 scf = 14.7 tons – 6. 3 tons = 8.4 tons PM 2.5 – (3,871.7 x 10 6 scf) x 7.6 lb/10 6 scf = 14.7 tons – 6.0 tons = 8.7 tons CH 4 – (3,871.7 x 10 6 scf) x 2.3 lb/10 6 scf = 4.5 tons – 0. 0 tons = 4.5 tons



### A-3SOURCE TYPE: Area SOURCE CATEGORY: Commercial/Institutional Fuel Combustion – Natural Gas

DESCRIPTION: Commercial/institutional combustion of natural gas. Emission sources include bo ilers, furnaces, heaters, internal combustion (IC) engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 , CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data ACTIVITY DATA:
• Annual natural gas quantity used in t he commercial sector (Vivo, 2008) EMISSION FACTORS:
• NO x– 100 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • SO x– 0.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • VOC – 5.5 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CO – 84 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 10 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 2.5 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CH 4 – 2.3 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998])

NOTES AND ASSUMPTIONS:
• None.
SAMPLE CALCULATIONS: Estimate annual emissions from commercial natural gas usage in Mexicali. Annual industrial natural gas usage = 1.462 x 10 6 m 3 = (51.6 x 10 6 scf) Annual municipality-level emissions – Mexicali : NO x– (51.64 x 10 6 scf) x 100 lb/10 6 scf = 2.58 tons SO x– (51.64 x 10 6 scf) x 0.6 lb/10 6 scf = 0.02 tons VOC – (51.64 x 10 6 scf) x 5.5 lb/10 6 scf = 0.14 tons CO – (51.64 x 10 6 scf) x 84 lb/10 6 scf = 2.17 tons PM 10 – (51.64 x 10 6 scf) x 7.6 lb/10 6 scf = 0.20 tons PM 2.5 – (51.64 x 10 6 scf) x 7.6 lb/10 6 scf = 0.20 tons CH 4 – (51.64 x 10 6 scf) x 2.3 lb/10 6 scf = 0.06 tons



### A-4SOURCE TYPE: Area SOURCE CATEGORY: Residential Fuel Combustion – Natural Gas

DESCRIPTION: Residential combustion of natural gas for heating and cooking. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 , CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Households ACTIVITY DATA:
• Annual natural gas quantity used in t he residential sector (Vivo, 2008) EMISSION FACTORS:
• NO x– 94 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • SO x– 0.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • VOC – 5.5 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CO – 40 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 10 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • PM 2.5 – 7.6 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998]) • CH 4 – 2.3 lb/10 6 scf (U.S. EPA, 1995 [Section 1.4 – Updated July 1998])

NOTES AND ASSUMPTIONS:
• None.
SAMPLE CALCULATIONS: Estimate annual emissions from residential natural gas usage in Mexicali. Annual residential natural gas usage = 4.417 x 10 6 m 3 = (156.01 x 10 6 scf) Annual municipality-level emissions – Mexicali : NO x– (156.01 x 10 6 scf) x 94 lb/10 6 scf = 7.33 tons SO x– (156.01 x 10 6 scf) x 0.6 lb/10 6 scf = 0.05 tons VOC – (156.01 x 10 6 scf) x 5.5 lb/10 6 scf = 0.43 tons CO – (156.01 x 10 6 scf) x 40 lb/10 6 scf = 3.12 tons PM 10 – (156.01 x 10 6 scf) x 7.6 lb/10 6 scf = 0.59 tons PM 2.5 – (156.01 x 10 6 scf) x 7.6 lb/10 6 scf = 0.59 tons CH 4 – (156.01 x 10 6 scf) x 2.3 lb/10 6 scf = 0.18 tons



### A-5SOURCE TYPE: Area SOURCE CATEGORY: Industrial Fuel Combustion – LPG

DESCRIPTION: Industrial combustion of liquefied petroleum gas (LPG). Emission sources include boilers, furnaces, heaters, internal combustion (IC) engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 ,and CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data

ACTIVITY DATA:
• Annual LPG used in the industrial sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008)
EMISSION FACTORS:
• NO x– 13.8 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • SO x– 0.0039 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • VOC – 0.84 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CO – 7.86 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • PM (PM 10 and PM 2.5 ) – 0.74 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CH 4 – 0.2 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) NOTES AND ASSUMPTIONS:
• Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) responded to requests for information. The fourth distributor (i.e., Gas Butano Propano de Baja California) did not respond. It was assumed that LPG distributed by Gas Butano Propano was equal to the average of the quantities distributed by Gas Silza and Kino Gas. • LPG is assumed to contain 60% propane and 40% butane. • Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft 3

. • Emission factor for total PM is assumed to represent PM 10 and PM 2.5 emissions. • Industrial area source LPG emissions were reconciled with t he industrial point source inventory by subtracting point source inventory LPG emissions from the area source LPG emissions.
SAMPLE CALCULATIONS: Estimate annual emissions from in dustrial LPG usage in Mexicali. Industrial LPG usage = 8,167,952 gallons/year Annual NO xemissions = 13.8 lbs/1,000 gallons × (8,167,9 52 gallons) = 56.4 tons – 0.1 tons = 56.3 tons Annual SO x emissions = 0.039 lbs/1,000 gallons × (8,167, 952 gallons) = 0.2 tons – 0.0 tons = 0.2 tons Annual VOC emissions = 0.84 lbs/1,000 gallons × (8 ,167,952 gallons) = 3.4 tons – 0.0 tons = 3.4 tons Annual CO emissions = 7.86 lbs/1,000 gallons × (8, 167,952 gallons) = 32.1 tons – 0.0 tons = 32.1 tons Annual PM 10 emissions = 0.74 lbs/1,000 ga llons × (8,167,952 gallons) = 3.0 tons – 0.0 tons = 3.0 tons Annual PM 2.5 emissions = 0.74 lbs/1,000 ga llons × (8,167,952 gallons) = 3.0 tons – 0.0 tons = 3.0 tons Annual CH 4 emissions = 0.2 lbs/1,000 gallo ns × (8,167,952 gallons) = 0.8 tons – 0.0 tons = 0.8 tons



### A-6SOURCE TYPE: Area SOURCE CATEGORY: Commercial Fuel Combustion – LPG

DESCRIPTION: Commercial combustion of liquefied petroleum gas (LPG). Emission sources include boilers, furnaces, heaters, internal combustion (IC) engines, etc. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 ,and CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• Annual LPG used in the commercial sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008)
EMISSION FACTORS:
• NO x– 13.8 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • SO x– 0.0039 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • VOC – 0.84 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CO – 7.86 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • PM (PM 10 and PM 2.5 ) – 0.74 lbs/1,000 gallo ns (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CH 4 – 0.2 lbs/1,000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) NOTES AND ASSUMPTIONS:
• Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) responded to requests for information. The fourth distri butor (i.e., Gas Butano Propano de Baja California) did not respond. It was assumed that LPG distributed by Gas Butano Propano was equal to t he average of the quantities distributed by Gas Silza and Kino Gas. • LPG is assumed to contain 60% propane and 40% butane. • Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft 3

. • Emission factor for total PM is assumed to represent PM 10 and PM 2.5 emissions.

SAMPLE CALCULATIONS: Estimate annual emissions from commercial LPG usage in Mexicali. Commercial LPG usage = 3,424,855 gallons/year Annual NO xemissions = 13.8 lbs/1,000 gallons × (3,424,855 gallons) = 23.6 tpy Annual SO x emissions = 0.039 lbs/1,000 gallo ns × (3,424,855 gallons) = 0.1 tpy Annual VOC emissions = 0.84 lbs/1,000 gallons × (3,424,855 gallons) = 1.4 tpy Annual CO emissions = 7.86 lbs/1,000 gallons × (3,424,855 gallons) = 13.5 tpy Annual PM 10 emissions = 0.74 lbs/1,000 gall ons × (3,424,855 gallons) = 1.3 tpy Annual PM 2.5 emissions = 0.74 lbs/1,000 gall ons × (3,424,855 gallons) = 1.3 tpy Annual CH 4 emissions = 0.2 lbs/1,000 gallons × (3,424,855 gallons) = 0.3 tpy



### A-7SOURCE TYPE: Area SOURCE CATEGORY: Residential Fuel Combustion – LPG

DESCRIPTION: Residential combustion of liquefied petro leum gas (LPG) for heating and cooking. POLLUTANTS: NO x , SO x , VOC, CO, PM 10 , PM 2.5 ,and CH 4 METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Annual LPG used in the residential sector (Álvarez Sánchez, 2008; Escalante, 2008; Félix Robles, 2008)
EMISSION FACTORS:
• NO x– 13.8 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • SO x– 0.0039 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • VOC – 0.84 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CO – 7.86 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • PM (PM 10 and PM 2.5 ) – 0.74 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) • CH 4 – 0.2 lbs/1000 gallons (U.S. EPA, 1995 [Section 1.5 – Updated July 2008]) NOTES AND ASSUMPTIONS:
• Three out of four LPG distributors (i.e., Gas Silza, Kino Gas del Noroeste, and Hidrocarburos del Noroeste) responded to requests for information. The fourth distributor (i.e., Gas Butano Propano de Baja California) did not respond. It was assumed that LPG distributed by Gas Butano Propano was equal to the average of the quantities distributed by Gas Silza and Kino Gas. • LPG is assumed to contain 60% propane and 40% butane. • Sulfur content of propane and butane is assumed to be 0.4114 gr/100 ft 3

. • Emission factor for total PM is assumed to represent PM 10 and PM 2.5 emissions. • Residential emission factors are assumed to be identical to commercial emission factors.
SAMPLE CALCULATIONS: Estimate annual emissions from residential LPG usage in Mexicali. Residential LPG usage = 14,923,787 gallons/year Annual NO xemissions = 13.8 lbs/1,000 gall ons × (14,923,787 gallons) = 103.0 tpy Annual SO x emissions = 0.039 lbs/1,000 gallo ns × (14,923,787 gallons) = 0.3 tpy Annual VOC emissions = 0.84 lbs/1,00 0 gallons × (14,923,787 gallons) = 6.3 tpy Annual CO emissions = 7.86 lbs/1,000 gal lons × (14,923,787 gallons) = 58.7 tpy Annual PM 10 emissions = 0.74 lbs/1,000 gall ons × (14,923,787 gallons) = 5.5 tpy Annual PM 2.5 emissions = 0.74 lbs/1,000 gall ons × (14,923,787 gallons) = 5.5 tpy Annual CH 4 emissions = 0.2 lbs/1,000 gallons × (14,923,787 gallons) = 1.5 tpy



### A-8SOURCE TYPE: Area SOURCE CATEGORY: Architectural Surface Coatings

DESCRIPTION: Surface coatings such as paints, paint primer, varn ish, or lacquer applied to architectural surfaces. POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• 2005 Mexicali popul ation (INEGI, 2006)
EMISSION FACTORS:
• VOC (solvent-based) – 0.7 28 lbs/person-yr (ERG, 2006) • VOC (water-based) – 0.391 lbs/person-yr (ERG, 2006) NOTES AND ASSUMPTIONS:
• Mexico-specific emission factors derived as part of the development of the 1999 Mexico National Emissions Inventory using data from ANAFAPYT (ANAFAPYT, 2003).
SAMPLE CALCULATIONS: Estimate annual emissions from architec tural surface coatings in Mexicali. Activity data : Mexicali population = 855,962 Annual municipality-level emissions : VOC emissions (solvent-based) = 0.728 lbs/ person-yr × (855,962 people) = 311.5 tons VOC emissions (water-based) = 0.391 lbs/pe rson-yr × (855,962 people) = 167.3 tons Total VOC emissions = 311.5 t ons + 167.3 tons = 478.8 tons



### A-9SOURCE TYPE: Area SOURCE CATEGORY: Industrial Surface Coating

DESCRIPTION: Coatings such as paints, varnish, lacquer, and paint primer. Surface coatings ar e applied to a wide variety of products, including furniture, cans, automobiles, ai rplanes and other transportation equipment, machinery, household appliances, flat wood, wire, and other miscellaneous products. In addition, coatings are used in maintenance operations at industrial facilities

. POLLUTANTS: VOC METHOD: Per employee emission factors derived from Mexico paint sales. SPATIAL ALLOCATION: Employee data by sector
ACTIVITY DATA:
• 2005 Mexicali employee counts (INEGI, 2005) • Wood and metal furniture (NAICS 33711x, 33712x, 33721x) • Other metallic products (NAICS 332xxx, 333xxx) • Electronics and electrical e quipment (NAICS 334xxx, 335xxx) • Automotive industry (NAI CS 3361xx, 3362xx, 3363xx) • Other transportation industry (NAICS 3364xx, 3365xx, 3366xx, 3369xx) • Other manufacturing industries (NAICS 339xxx) • Industrial maintenance coatings (NAICS 31xxxx, 32xxxx, 33xxxx) EMISSION FACTORS:

• Per employee emission factors (ERG, 2006) • VOC – Wood and metal furniture – 270.24 lbs/employee-yr • VOC – Other metallic produc ts – 225.13 lbs/employee-yr • VOC – Electronics and electrical equipment – 3.33 lbs/employee-yr • VOC – Automotive industry – 376.19 lbs/employee-yr • VOC – Other transportation indus try – 256.66 lbs/employee-yr • VOC – Other manufacturing indust ries – 342.82 lbs/employee-yr • VOC – Industrial maintenance c oatings – 13.98 lbs/employee-yr NOTES AND ASSUMPTIONS:
• Mexico-specific emission factors derived as part of the development of the 1999 Mexico National Emissions Inventory using data from ANAFAPYT (ANAFAPYT, 2003). • Reconciliation with the point source inventory was conduct ed by subtracting point source emission estimates from any point sources with NAICS codes covered by the industrial surface coati ng area source category.
SAMPLE CALCULATIONS: Estimate annual emissions from industrial surface coating in Mexicali. Activity data : Mexicali employee count = 547 (wood and metal furniture) Mexicali employee count = 8,990 (other metallic products) Mexicali employee count = 19,677 (electronics and electrical equipment) Mexicali employee count = 6,812 (automotive industry) Mexicali employee count = 2,550 (other transportation industry) Mexicali employee count = 7,367 (other manufacturing industries) Mexicali employee count = 62,015 (industrial maintenance coatings) Annual municipality-level emissions : VOC emissions = 270.24 lbs/employee-yr × (547 empl oyees) = 73.9 tons – 9.1 tons = 64.8 tons VOC emissions = 225.13 lbs/employee-yr × (8,990 empl oyees) = 1,012.0 tons – 4. 9 tons = 1,007.1 tons VOC emissions = 3.33 lbs/employee-yr × (19,677 em ployees) = 32.8 tons – 0.3 tons = 32.5 tons VOC emissions = 376.19 lbs/employee-yr × (6,812 empl oyees) = 1,281.3 tons – 0. 1 tons = 1,281.2 tons VOC emissions = 256.66 lbs/employee-yr × (2,550 em ployees) = 327.2 tons – 0.0 tons = 327.2 tons VOC emissions = 342.82 lbs/employee-yr × (7,367 empl oyees) = 1,262.8 tons – 0. 2 tons = 1,262.6 tons VOC emissions = 13.98 lbs/employee-yr × (62,015 employees) = 433.5 tons – 0.0 tons = 433.5 tons Total VOC emissions = 64.8 tons + 1,007. 1 tons + 32.5 tons + 1,281.2 tons + 327. 2 tons + 1,262.6 t ons + 433.5 tons = 4,408.9 tons 

### A-10SOURCE TYPE: Area SOURCE CATEGORY: Autobody Refinishing

DESCRIPTION: Repair and restoration of automobiles, light trucks, and other vehicle bodies. Mo st repair jobs include refinishing on a portion of the vehicle; new vehicle coatin g is excluded from this source category. POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• 2005 Mexicali employ ee count (NAICS 811121) (INEGI, 2005)
EMISSION FACTORS:
• VOC – 277.25 lbs/employee-yr (ERG, 2006) NOTES AND ASSUMPTIONS:
• Mexico-specific emission factor derived as part of the dev elopment of the Mexico National Emissions Inventory using data from ANAFAPYT (ANAFAPYT, 2003).
SAMPLE CALCULATIONS: Estimate annual emissions from autobody refinishing in Mexicali. Activity data : Mexicali employee count = 568 Municipality-level emissions : Annual VOC emissions = 277.25 lbs/empl oyee-yr × (568 employees) = 78.7 tons Total VOC emissions = 78.7 tons



### A-11SOURCE TYPE: Area SOURCE CATEGORY: Graphic Arts

DESCRIPTION: Various graphic arts processes including typography, o ffset (web and sheet), rotogravure, silk screening, and flexography. POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• 2005 Mexicali popul ation (INEGI, 2006)
EMISSION FACTORS:
• VOC – 0.811 lbs/person-yr (ERG, 2006) NOTES AND ASSUMPTIONS:
• Mexico-specific emission factor derived as part of the dev elopment of the Mexico National Emissions Inventory using data from ANAFAPYT (ANAFAPYT, 2004).
SAMPLE CALCULATIONS: Estimate annual emissions from graphic arts in Mexicali. Activity data : Mexicali population = 855,962 Municipality-level emissions : Annual VOC emissions = 0.811 lbs/pers on-yr × (855,962 people) = 346.9 tons Total VOC emissions = 346.9 tons



### A-12SOURCE TYPE: Area SOURCE CATEGORY: Traffic Markings

DESCRIPTION: Various emission sources in this source category include pa inting of centerlines, edge stripes, directional markings, and paved and unpaved surfaces to improve traffic flow. These include solvent- and water-based paints. POLLUTANTS: VOC METHOD: Estimates from Mexico NEI (ERG, 2006) SPATIAL ALLOCATION: Paved road network
ACTIVITY DATA:
• Not applicable
EMISSION FACTORS:
• Not applicable NOTES AND ASSUMPTIONS:
• Data request for quantities of traffic markings was made to the Mexicali Public Works, but data were not available (Reynoso Ambriz, 2008). • Mexicali emission estimates were used from the Mexico NEI. • Emissions from traffic markings were assumed to be unchanged from 1999 to 2005.
SAMPLE CALCULATIONS: Estimate the total annual emissions from traffic marki ngs in Mexicali. Calculations from 1999 Mexico NEI. National level emissions : Total VOC emissions = 3,031.8 Mg/year State level emissions : National paved road length = 237,635 km Paved road length in Baja California = 6,805.7 km Annual VOC emissions = (6,805.7 km/ 237,635 km) × 3,031.8 Mg = 86.8 Mg Municipality level emissions – Mexicali : State paved road length = 6,805.7 km Paved road length in Mexicali = 4,057.4 km Annual VOC emissions = (4,057.4 km/6,805 .7 km) × 86.8 Mg = 51.8 Mg = 57.1 tons



### A-13SOURCE TYPE: Area SOURCE CATEGORY: Dry Cleaning

DESCRIPTION: Solvent evaporation during dry cleaning process, from leak s in the equipment, and from solvent recovery or disposal systems. POLLUTANTS: VOC METHOD: Per employee emission factors. SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• 2005 Mexicali employ ee count (NAICS 8122 1x) (INEGI, 2005)
EMISSION FACTORS:
• VOC – 700.66 lbs/employee-yr (ERG, 2006) NOTES AND ASSUMPTIONS:
• Only dry cleaning facilities using petroleum-based organic solvents for cleaning are included in this category; dry cleaning facilities using perchloroethylene are excluded. • Mexico-specific emission factor derived as part of the dev elopment of the Mexico National Emissions Inventory using data from CANALAVA (CANALAVA, 2002).
SAMPLE CALCULATIONS: Estimate annual emissions from dry cleaning in Mexicali. Activity data : Mexicali employee count = 416 Municipality-level emissions : Annual VOC emissions = 700.66 lbs/empl oyee-yr × (416 employees) = 145.7 tons Total VOC emissions = 145.7 tons



### A-14SOURCE TYPE: Area SOURCE CATEGORY: Degreasing

DESCRIPTION: Volatile organic compound emissions from surface cleaning operations. Surface cleaning operations involve the use of solvent liquids or solvent vapors to remove water-insoluble contaminants such as grease, oils, waxes, carbon deposits, fluxes, and tars from metal, plastic, glass, and other surfaces. POLLUTANTS: VOC METHOD: Per employee emission factors SPATIAL ALLOCATION: Employee data
ACTIVITY DATA:
• 2005 Mexicali employee counts (INEGI, 2005) • Cold cleaning (auto repair) (NAICS 8111xx) • Cold cleaning (manufacturing) (NAICS 31xxxx-33xxxx) • Vapor and in-line cleaning (electronics and electrical) (NAICS 334xxx-335xxx) • Vapor and in-line cleaning (other) (NAICS 31xxxx-33xxxx; 8111xx) EMISSION FACTORS:
• Per employee emission factors (EIIP, 1997) • VOC – Cold cleaning (auto repair) – 270 lbs/employee-yr • VOC – Cold cleaning (manufacturing) – 24 lbs/employee-yr • VOC – Vapor and in-line cleaning (electronics and electrical) – 29 lbs/employee-yr • VOC – Vapor and in-line cleaning (other) – 9.8 lbs/employee-yr NOTES AND ASSUMPTIONS:
• It was assumed that Mexican degreasing operati ons were similar to those in the U.S. • Reconciliation with the point source inventory was conduct ed by subtracting point source emission estimates from any point sources with NAICS codes covered by the degreasing area source category.
SAMPLE CALCULATIONS: Estimate annual emissions from degreasing in Mexicali. Activity data : Mexicali employee count = 4,186 (cold cleaning – auto repair) Mexicali employee count = 62,015 (cold cleaning – manufacturing) Mexicali employee count = 19,677 (vapor and in-line cleaning – electronics and electrical) Mexicali employee count = 66,201 (vapor and in-line cleaning – other) Annual municipality-level emissions : VOC emissions = 270 lbs/employee-yr × (4,186 empl oyees) = 565.1 tons – 0.0 tons = 565.1 tons VOC emissions = 24 lbs/employee-yr × (62,015 empl oyees) = 744.2 tons – 0.0 tons = 744.2 tons VOC emissions = 29 lbs/employee-yr × (19,677 empl oyees) = 285.3 tons – 0.6 tons = 284.7 tons VOC emissions = 9.8 lbs/employee-yr × (66,201 employees) = 324.4 tons – 0.9 tons = 323.5 tons Total VOC emissions = 565.1 tons + 744.2 tons + 284.7 tons + 323.5 tons = 1,917.4 tons



### A-15SOURCE TYPE: Area SOURCE CATEGORY: Consumer Solvent Use

DESCRIPTION: Consumer use of various solvent-cont aining products, including: personal care products, household products, automotive aftermarket products, adhesiv es and sealants, FIFRA-regulated produc ts, coatings and related products, and miscellaneous products POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• 2005 Mexicali popul ation (INEGI, 2006)
EMISSION FACTORS:
• VOC (personal care products) – 2.32 lbs/person-yr (EIIP, 1996) • VOC (household products) – 0.20 lb s/person-yr (EIIP, 1996; note 1) • VOC (automotive aftermarket products) – 0.16 lbs/person-yr (EIIP, 1996; note 2) • VOC (adhesives and sealants) – 0.57 lbs/person-yr (EIIP, 1996) • VOC (FIFRA-regulated products – 1.78 lbs/person-yr (EIIP, 1996) • VOC (coatings and related products ) – 0.96 lbs/person-yr (EIIP, 1996) • VOC (miscellaneous products) – 0.07 lbs/person-yr (EIIP, 1996) NOTES AND ASSUMPTIONS:
• Note 1 – EIIP emission factor for household products adjust ed by a factor of 0.25 bas ed upon the pervasive use of bleach as a cleaning agent instead of typical solvent-c ontaining products (Fierro and O’Rourke, 2007; Fierro, 2007). • Note 2 – EIIP emission factor for automotive aftermarket pro ducts adjusted by a factor of 0.12 based upon the use ofwater instead of windshield fluids from the area source residual quantities (BTS, 2007; U.S. Census, 2007; GonzalezAyala, 2007).
SAMPLE CALCULATIONS: Estimate annual emissions from consumer products in Mexicali. Activity data : Mexicali population = 855,962 Municipality-level emissions : Annual VOC emissions (personal care products) = 2.32 lbs/person-yr × (855, 962 people) = 993.2 tons Annual VOC emissions (household products) = 0. 20 lbs/person-yr × (855,9 62 people) = 84.6 tons Annual VOC emissions (automotive aftermarket products ) = 0.16 lbs/person-yr × ( 855,962 people) = 67.6 tons Annual VOC emissions (adhesives and sealants) = 0.57 lbs/person-yr × (855,962 people) = 243.0 tons Annual VOC emissions (FIFRA-regulated products) = 1.78 lbs/person-yr × (855, 962 people) = 761.1 tons Annual VOC emissions (coatings and related products ) = 0.96 lbs/person-yr × ( 855,962 people) = 409.6 tons Annual VOC emissions (miscellaneous products) = 0.96 lbs/person-yr × (855, 962 people) = 29.6 tons Total VOC emissions = 993.2 tons + 84.6 tons + 67.6 tons + 243.0 tons +761.1 tons + 409.6 tons + 29.6 tons = 2,588.7 tons



### A-16SOURCE TYPE: Area SOURCE CATEGORY: Asphalt Application

DESCRIPTION: Asphalt application to surfaces and pavements result s in hydrocarbon emissions due to evaporation. POLLUTANTS: VOC METHOD: Use of 1999 Mexico National Emissions Inventory (ERG, 2006) SPATIAL ALLOCATION: Paved road network
ACTIVITY DATA:
• National-level emissions (ERG, 2006) • National and munici pality level paved road lengths (INEGI, 2002) EMISSION FACTORS:
• Not applicable NOTES AND ASSUMPTIONS:
• Cutback fraction of total asphalt is assumed to be similar to the U.S. (i.e., 3.06% of total asphalt). • Asphalt is assumed to be a medium-cure cutback. • Diluent content is assumed to be 35% (EIIP, 2001d). • Diluent density is assumed to be 0.8 kg/liter (EIIP, 2001d). • Evaporated fraction of diluent is assumed to be 75% (EIIP, 2001d). • Emissions from hot-mix and emulsified asphalts are assumed to be negligible (EIIP, 2001d).

SAMPLE CALCULATIONS: Estimate annual emissions from asphalt application in Baja California. National level emissions : National VOC emissions = 7,756.0 Mg/year

State level emissions : National paved road length = 237,635 km State paved road length = 6,805.7 km Annual VOC emissions in Baja California = 7,756.0 Mg/year × (6,805.7 km/237,635 km) = 222.1 Mg Municipality level emissions – Mexicali : State paved road length = 6,805.7 km Paved road length in Mexicali = 4,057.4 km Annual VOC emissions in Mexicali = 222.1 Mg × (4,057.4 km/6,805.7 km) = 132.4 Mg = 146.0 tons



### A-17SOURCE TYPE: Area SOURCE CATEGORY: Gasoline Distribution

DESCRIPTION: This source category relates to the emissions occurring dur ing the transportation and dist ribution of gasoline. This category includes Stage I loading losses (from tank tru ck to underground tank), Stage II loading losses (from underground tank to vehicle – including spillage), under ground tank breathing, and tank truck transit losses. POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Gasoline (Magna and Premium) quantity sold (Pa rra Armenta, 2008; Parra Armenta, 2009)
EMISSION FACTORS:
• VOC Stage I controlled – 40 mg/liter (EIIP, 2001a) • VOC Stage II uncontrolled – 1320 mg/liter (EIIP, 2001a) • VOC Spillage – 80 mg/liter (EIIP, 2001a) • VOC Tank losses – 120 mg/liter (EIIP, 2001a) • VOC Gas filled trucks – 1 mg/liter (EIIP, 2001a) • VOC Vapor filled trucks – 13 mg/liter (EIIP, 2001a) NOTES AND ASSUMPTIONS:
• It was assumed that all stations in Mexicali have Stage I controls (assumed to be vapor balance) (Ruiz Méndez, 2003). • It was assumed that all stations in Mexicali do not have Stage II controls (Ruiz Méndez, 2003).
SAMPLE CALCULATIONS: Estimate annual VOC emissions from gasoline distribution in Mexicali. Mexicali Magna gasoline = 224,800,401 liters Mexicali Premium gasoline = 63,773,310 liters Total gasoline = 224,800,401 liters + 63, 773,310 liters = 288,573,711 liters Mexicali emissions : Stage I controlled emissions = 40 mg/l x 288,573,711 liters = 11.54 Mg = 12.7 tons Stage II uncontrolled emissions = 1,320 mg/l x 288,573,711 liters = 380.92 Mg = 419.9 tons Spillage emissions = 80 mg/l x 288,573, 711 liters = 23.09 Mg = 25.4 tons Tank losses = 120 mg/l x 288,573,71 1 liters = 34.63 Mg = 38.2 tons Tank truck transit losses (gas-filled) = 1 mg/l x 288,573,711 liters = 0.29 Mg = 0.3 tons Tank truck transit losses (vapor-filled) = 13 mg /l x 288,573,711 liters = 3.75 Mg = 4.1 tons Total Mexicali gasoline distribution emissions = 12.7 tons + 419. 9 tons + 25.4 tons + 38.2 t ons + 0.3 tons + 4.1 tons = 500.7 tons



### A-18SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Burning

DESCRIPTION: Emissions from burning of agricultural residue. POLLUTANTS: NO x , SO 2 , VOC, CO, PM 10 , and PM 2.5 METHOD: Emission factors SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Burn acreage by crop type (Rodríguez Hernández, 2008) • Fuel loading by crop type (wheat 1.9 tons/acre, sorghum 2.9 tons/acre, miscellaneous weeds 2.175 tons/acre) (ARB, 2005) EMISSION FACTORS:
• Wheat (NO x – 4.3 lbs/ton, SO 2 – 0.9 lbs/ton, VOC – 7.6 lb s/ton, CO – 123.6 lbs/ton, PM 10 – 10.6 lbs/ton, PM 2.5 – 10.1 lbs/ton) (ARB, 2005) • Sorghum (NO x – 4.5 lbs/ton, SO 2 – 0.6 lbs/ton, VOC – 5.10 lb s/ton, CO – 77 lbs/ton, PM 10 – 17.7 lbs/ton, PM 2.5 – 16.9 lbs/ton) (ARB, 2005) • Irrigation canal and ditchbank weeds (NO x– 4.49 lbs/ton, SO 2 – 0.61 lbs/ton, VOC – 10. 73 lbs/ton, CO – 113.95 lbs/ton, PM 10 – 15.9 lbs/ton, PM 2.5 – 15.18 lbs/ton) (ARB, 2005) NOTES AND ASSUMPTIONS:
• Only wheat, sorghum, and irrigation canal and ditch bank weeds are burned in Mexicali.
SAMPLE CALCULATIONS: Estimate annual emissions from agricultural burning of wheat. Area burned = 57,000 hectares = 140,847 acres Residue burned = 140,847 acres x 1.9 tons/acre = 267,609 tons Mexicali emissions : NO x – (267,609 tons) x 4. 3 lb/ton = 575.4 tons SO x – (267,609 tons) x 0.9 lb/ton = 120.4 tons VOC – (267,609 tons) x 7. 6 lb/ton = 1,016.9 tons CO – (267,609 tons) x 12 3.6 lb/ton = 16,538.3 tons PM 10 – (267,609 tons) x 10.6 lb/ton = 1,418.3 tons PM 2.5 – (267,609 tons) x 10.1 lb/ton = 1,351.4 tons Total NO xemissions = wheat emissions + sorghum emissions + ditchbank weed emissions = 575.4 tons + 38.7 tons + 7.2 tons = 621.3 tons



### A-19SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Tilling

DESCRIPTION: Fugitive dust emissions from agricultural tilling. POLLUTANTS: PM 10 and PM 2.5 METHOD: Emission factors SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Total planted area by crop type (SAGARPA, 2007a)
EMISSION FACTORS : • Crop-specific emission factors (lbs/acre-yr) (ARB, 2003a).

NOTES AND ASSUMPTIONS:
• PM 2.5 particle size fraction of PM 10 is 0.2217 (ARB, 2002). • It is assumed that Mexican agricultural tilling practices are si milar to practices in the San Joaquin Valley, California.
SAMPLE CALCULATIONS: Estimate annual emissions from agricultura l tilling operations for wheat in Mexicali. Total planted area for wheat (avera ge hard, soft, and crystalline varietie s) = 81,111 hectares = 200,425 acres Annual PM 10 emissions = 200,425 acres x 3.7 lbs/ac re-yr = 741,572.5 lbs/yr = 370.8 tons/yr Annual PM 2.5 emissions = 370.8 tons PM 10 x 0.2217 = 82.2 tons/yr



### A-20SOURCE TYPE: Area SOURCE CATEGORY: Agricultural Harvesting

DESCRIPTION: Fugitive dust emissions from agricultural harvesting. POLLUTANTS: PM 10 and PM 2.5 METHOD: Emission factors SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Total harvested area by crop type (SAGARPA, 2007a)
EMISSION FACTORS : • Crop-specific emission factors (lbs/acre-yr) (ARB, 2003b).

NOTES AND ASSUMPTIONS:
• PM 2.5 particle size fraction of PM 10 is 0.2217 (ARB, 2002). • It is assumed that Mexican agricultural harvesting practi ces are similar to practices in the San Joaquin Valley, California.
SAMPLE CALCULATIONS: Estimate annual emissions from agricultural harvest operations for wheat in Mexicali. Total harvested area for wheat (average hard, soft, and cr ystalline varieties) = 80,555 hectares = 199,051 acres Annual PM 10 emissions = 199,051 acres x 5.8 lbs/acre -yr = 1,154,498.1 lbs/yr = 577.2 tons/yr Annual PM 2.5 emissions = 577.2 tons PM 10 x 0.2217 = 128.0 tons/yr



### A-21SOURCE TYPE: Area SOURCE CATEGORY: Pesticides

DESCRIPTION: Volatile organic compound emissions from the active a nd inert ingredients in pesticides. VOC emissions can occur either during application of pestici des or as a result of evaporation. POLLUTANTS: VOC METHOD: Emission factors SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Municipality level crop acreage (SAGARPA, 2007a) • Typical application rates (SAGARPA, 2007b) EMISSION FACTORS:
• VOC emission factors (active ingredients only) – 700 kg/Mg (vapor pressure between 0.0001 mm Hg and 0.000001 mm Hg) and 1,160 kg/Mg (vapor pressure greater than 0.0001 mm Hg) (EIIP, 2001b) NOTES AND ASSUMPTIONS:
• Method of pesticide application was assumed to be surface application. • Typical application rates were only available for alfalfa, asparagus, broccoli, cotton, garlic, tomatoes (red and green), and wheat.
SAMPLE CALCULATIONS: Estimate the total annual emissions from pesticide application for wheat in Mexicali. Wheat acreage = 81,111 hectares = 200,425 acres Dimetoato (dimethoate) applied at a rate of 1.5 liters/hectare (converted to 1.46 lbs/acre using pesticide density) 43.5% active; 56.5% inert Active ingredient vapor pressure – 2.5 x 10 -5 mm; corresponds to emission factor of 700 lbs VOC/ton of active ingredient Applied as an emulsified concentrate; corresponds to a inert ingredient VOC content of 56% Emissions from active ingredients : Emissions = 200,425 acres x 1.46 lbs pest icide/acre x 0.435 x 700 lbs VOC/ton of active ingredients = 44,535.2 lbs = 22.3 tons Emissions from inert ingredients : Emissions = 200,425 acres x 1.46 lbs pesticide/acre x 0.565 x 0. 56 = 92,551.4 lbs = 46.3 tons Total emissions : Total emissions = active emissions + inert em issions = 22.3 tons + 46.3 tons = 68.6 tons



### A-22SOURCE TYPE: Area SOURCE CATEGORY: Beef Cattle Feedlots

DESCRIPTION: Fugitive dust emissions released because cattle movement over ground. POLLUTANTS: PM 10 METHOD: Emission factor SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Number of cattle slaughtered (Rodríguez Hernández) • Residence time in feedlot – 90 days (Sukarne, 2008) EMISSION FACTORS : • 28.9 lbs PM 10 /1000 head-day (ARB, 2004) NOTES AND ASSUMPTIONS:
•
SAMPLE CALCULATIONS:
Estimate the total annual emissions from beef cattle feedlots. Cattle slaughtered = 223,545 Residence time = 90 days Emissions = 223,545 head x 90 days x 28.9 lbs PM 10 /1000 head-day = 581,440.5 lbs PM 10 = 290.7 tons PM 10



### A-23SOURCE TYPE: Area SOURCE CATEGORY: Livestock Ammonia and VOC

DESCRIPTION: The sources of emissions in this category are livestock and domesticated farm and dairy animals. POLLUTANTS: VOC and NH 3 METHOD: Emission factors SPATIAL ALLOCATION: Agricultural land use
ACTIVITY DATA:
• Livestock population (SAGARPA, 2007c) EMISSION FACTORS:
• VOC: Beef cattle – 12.8 lb/head-year ; dairy cows – 12.8 lb/head-year; pigs – 4.6 lb/head-year; sheep – 0.96 lb/headyear; goats – 0.96 lb /head-year (ARB, 2004) • NH 3 : Beef cattle – 9.0 kg/head-year; dairy cows – 25.0 kg/ head-year; pigs – 7.0 kg/head-year; sheep – 1.34 kg/head year; goats – 0.58 kg/head-year (Chinkin et al., 2003) ASSUMPTIONS:
• Livestock statistics represent year-round livestock populations. • Poultry and horse populations were not found in Mexicali. ­

SAMPLE CALCULATIONS: Estimate the total annual livestock ammonia emissions in Mexicali. Municipality level VOC emissions – Mexicali : Emissions from beef cattle = (160,221 head) × (12.8 lb VOC/head- year) = 1,025.4 tons Emissions from dairy cows = (50,432 head ) × (12.8 lb VOC/head- year) = 322.8 tons Emissions from pigs = (4,750 head) × (4.6 lb VOC/head-year) = 10.9 tons Emissions from sheep = (9,227 head) × (0.96 lb VOC/head-year) = 4.4 tons Emissions from goats = (5,781 head) × (0.96 lb VOC/head-year) = 2.8 tons Total annual VOC emissions = 1,025.4 + 322. 8 + 10.9 + 4.4 + 2. 8 = 1,366.3 tons Municipality level NH 3 emissions – Mexicali : Emissions from beef cattle = (160,221 head) × (9.0 kg NH 3 /head-year) = 1,441,989 kg = 1,589.5 tons Emissions from dairy cows = (50,432 head) × (25.0 kg NH 3 /head-year) = 1,260,800 kg = 1,389.8 tons Emissions from pigs = (4 ,750 head) × (7.0 kg NH 3 /head-year) = 33,250 kg = 36.7 tons Emissions from sheep = (9,227 head) × (1.34 kg NH 3 /head-year) = 12,364 kg = 13.6 tons Emissions from goats = (5,781 head) × (0.58 kg NH 3 /head-year) = 3,353 kg = 3.7 tons Total annual NH 3 emissions = 1,589.5 + 1,389.8 + 36.7 + 13.6 + 3.7 = 3,033.3 tons



### A-24Area SOURCE CATEGORY: Fertilizer Application

DESCRIPTION: Ammonia emissions from the application of nitrogen based fertilizers. The amount of ammonia emissions depend on the nitrogen content of the fertilizer. POLLUTANTS: NH 3 METHOD: Emission factors
ACTIVITY DATA:
• Municipality level crop acreage (SAGARPA, 2007a) • Typical application rates (SAGARPA, 2007b)

EMISSION FACTORS:
• NH 3 emission factors (kg NH 3 /kg fertilizer): anhydrous ammonia – 0.048 57; urea – 0.18214; NPK, calcium nitrate, diammonium phos phate – 0.0242 8 (EEA, 2002) NOTES AND ASSUMPTIONS:
• Typical application rates were only available for alfalfa, asparagus, broccoli, cotton, garlic, tomatoes (red and green), and wheat.
SAMPLE CALCULATIONS:
Estimate the total annual fertilizer emissions for wheat in Mexicali. Planted wheat area = 81,111 hectares Application rates = anhydrous ammonia 125 kg/hectare , urea 252 kg/hectare, 11-52-00 (N-P-K) 100 kg/hectare
NH 3 emissions = (81,111 hectares x 125 kg/hectare x 0.04857 kg NH 3 /kg fertilizer) + (81,111 hectares x 252 kg/hectare x 0.18214 kg NH 3 /kg fertilizer) + (81,111 hectares x 100 kg/hectare x 0.02428 kg NH 3 /kg fertilizer) = 4,412,319 kg NH 3 = 4,863.7 tons



### A-25SOURCE TYPE: Area SOURCE CATEGORY: Paved Road Dust

DESCRIPTION: Re-entrained road dust from vehicle activity on paved roads. POLLUTANTS: PM 10 , PM 2.5 METHOD: Emission factor equations SPATIAL ALLOCATION: Paved road network
ACTIVITY DATA:
• Paved road VMT (Gonzalez-Ayala, 2008a)
EMISSION FACTORS:
• Predictive emission factor equation (U.S. EPA, 1995) • k = particle size multiplier – 0.016 lb/VMT (PM 10 ); 0.0024 lb/VMT (PM 2.5 ) • sL = silt loading – derived from Mexicali paved road dust sampling at 10 locations • W = average vehicle weight • C = exhaust emission correction – 0.00047 lb/VMT (PM 10 ); 0.00036 lb/VMT (PM 2.5 ) • P = number of wet days (>0.01 in) = 21 • N = number of days in averaging period = 365 NOTES AND ASSUMPTIONS:
• It was assumed that the average vehicle weight was 3.18 tons. • Emission factors were calculated for each of the 10 locations and then averaged together. • Negative emission factors calculated with the pred ictive emission factor eq uation were set to zero.
SAMPLE CALCULATIONS:
Estimate PM 10 emission factor for Avenida Oaxaca between Calle Uxmal and Heroico Colegio Militar Length sampled = 216 feet Width sampled = 10 feet Sample weight = 681.5 g Silt content = 2.68% Emission factor = [k(sL/2) 0.65 (W/3) 1.5 – C] x (1 – P/4N) sL = silt/area = (681.5 g x 0.0268)/( 216 feet x 10 f eet) = 0.091025 g/m 2 EF(PM 10 ) = [(0.016) x (0.091025/2) 0.65 x (3.18/3) 1.5 – 0.00047] x (1 – 21/[4 x 365]) = 0.00185 lbs/VMT



### A-26SOURCE TYPE: Area SOURCE CATEGORY: Unpaved Road Dust

DESCRIPTION: Re-entrained road dust from vehicle activity on unpaved roads. POLLUTANTS: PM 10 , PM 2.5 METHOD: Emission factor equations SPATIAL ALLOCATION: Unpaved road network
ACTIVITY DATA:
• Unpaved road VMT (Gonzalez-Ayala, 2008b)
EMISSION FACTORS:
• Predictive emission factor equation (U.S. EPA, 1995) • k = empirical constants – 1.8 lb/VMT (PM 10 ); 0.18 lb/VMT (PM 2.5 ) • a = empirical constant – 1 (PM 10 and PM 2.5 ) • c = empirical constant – 0.2 (PM 10 and PM 2.5 ) • d = empirical constant – 0.5 (PM 10 and PM 2.5 ) • s = surface material silt content (%) – derived from Mexicali unpaved road dust sampling at 10 locations • S = mean vehicle speed (mph) – 11 mph urban; 18 mph suburban; 25 mph rural • M = surface material moisture content average vehicle weight (%) • C = exhaust emission correction – 0.00047 lb/VMT (PM 10 ); 0.00036 lb/VMT (PM 2.5 ) • P = number of wet days (>0.01 in) = 21 • N = number of days in averaging period = 365 NOTES AND ASSUMPTIONS:
• Urban, suburban, and rural emission factor s were calculated for each of the 10 locations and then averaged to obtain average urban, suburban, and rural emission factors. • Negative emission factors calculated with the pred ictive emission factor eq uation were set to zero.
SAMPLE CALCULATIONS:
Estimate PM 10 emission factor (urban at 11 mph) for Calle Texcoco between Calzada Laguna de Xochimilco and Avenida Acolman
Silt content = 1.66% Moisture content = 0.29% Emission factor = {[k(s/12) a (S/30) d ] / [(M/0.5) c ] – C} x (1 – [P/N]) EF(PM 10 ) = {[(1.8) x (1.66/12) 1 x (11/30) 0.5 ] / [(0.29/0.5) 0.2 ] – 0.00047} x (1 – [21/ 365]) = 0.15802 lbs/VMT



### A-27SOURCE TYPE: Area SOURCE CATEGORY: Structure Fires

DESCRIPTION: Structure fires include the unintentional burning of the structure material and building contents. POLLUTANTS: NO x , VOC, CO, PM 10 , and PM 2.5 METHOD: Emission factors SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Fires (Ruiz Burgueño, 2008) EMISSION FACTORS:
• NO x– 1.4 lbs/ton of material (EIIP, 2001c) • VOC – 11 lbs/ton of material (EIIP, 2001c) • CO – 60 lbs/ton of material (EIIP, 2001c) • PM 10 – 10.6 lbs/ton of material (EIIP, 2001c) • PM 2.5 – 9.9 lbs/ton of material (EIIP, 2001c) • CH 4 – 2.9 lbs/ton of material (EIIP, 2001c) NOTES AND ASSUMPTIONS:
• Average content loss was assumed to be 7.3% (EIIP, 2001c). • Average amount of combustible contents in the structure was assumed to be 38.62 kg/m 2 (EIIP, 2001c). • Average area of a structure was assumed to be 100 m 2

. • Only residential building fires were considered. • Combustible building material was assumed to be 0 Mg (masonry construction). • PM 2.5 size fraction of PM 10 = 0.9327 (ARB, 2002).
SAMPLE CALCULATIONS: Estimate the total annual emissions from structure fires in Mexicali. Annual municipality level emissions : Number of fires = 498 Total combustible material = number of fires × structural loss × (combustible building material + combustible building contents) = 498 × (0.073) × (0 + 100 m 2 × 38.62 kg/m 2 ) = 140,399 kg = 154.8 tons NO xemissions = 154.8 tons × 1.4 lbs/ton = 217 lbs = 0.1 tons VOC emissions = 154.8 tons × 11 lbs/ton = 1,703 lbs = 0.9 tons CO emissions = 154.8 tons × 60 lbs/ton = 9,288 lbs = 4.6 tons PM 10 emissions = 154.8 tons × 10.6 lbs/ton = 1,641 lbs = 0.8 tons PM 2.5 emissions = 154.8 tons × 9.9 lbs/ton = 1,533 lbs = 0.8 tons CH 4 emissions = 154.8 tons × 2.9 lbs/ton = 449 lbs = 0.2 tons



### A-28SOURCE TYPE: Area SOURCE CATEGORY: Border Crossings

DESCRIPTION: Emissions from motor vehicles idling at border crossings. POLLUTANTS: NO x , SO 2 , VOC, CO, PM 10 , PM 2.5 , NH 3 METHOD: Emission factors derived from MOBILE6-Mexico (ERG, 2003) SPATIAL ALLOCATION: Border crossing location
ACTIVITY DATA:
• Monthly average minimum and maximum temperatures • Number of vehicles at border crossing points (BTS, 2008) • Vehicle wait times at various border crossing ports (BTS, 2008) EMISSION FACTORS:
• MOBILE6-Mexico (ERG, 2003) NOTES AND ASSUMPTIONS:
• Average vehicle speed at border crossing points is assumed to be 4 km/hr. • Emissions were only estimated for vehicles ente ring from Mexico into the United States.
SAMPLE CALCULATIONS: Estimate NO x emissions from Calexico border crossing in January Number of LDGVs = 268,892 Number of LDGTs = 250,583 Number of MCs = 7,261 Number of HDDVs = 125 Speed – 0.0414 miles/minute (4 km/hour) Wait times – 43 minutes for LDGVs, LDGTs, and MCs; 2 minutes for HDDVs LDGV emission factor = 1.08 g NO x /mile LDGT emission factor = 1.328 g NO x /mile MC emission factor = 1.34 g NO x /mile HDDV emission factor = 15.959 g NO x /mile Emissions = (268,892 x 43 min x 0.0414 m iles/min x 1.08 g/mile) + (250,583 x 43 min x 0.0414 miles/mi n x 1.328 g/mile) + (7,261 x 43 min x 0.0414 miles/min x 1.34 g/mile) + (125 x 2 min x 0.0414 miles/min x 15.959 g/mile) = 1.127 Mg = 1.243 tons



### A-29SOURCE TYPE: Area SOURCE CATEGORY: Brick Kilns

DESCRIPTION: Emissions released during the firing of brick kilns. POLLUTANTS: NO x , SO 2 , VOC, CO, PM 10 , PM 2.5 , NH 3 METHOD: Utilization of previous emission estimates from 2005 San Luis Río Colorado (SLRC)/Mexicali emissions inventory (ENVIRON and ERG, 2008) SPATIAL ALLOCATION: Brick kiln location
ACTIVITY DATA:
• Not applicable
EMISSION FACTORS : • Not applicable NOTES AND ASSUMPTIONS:
• The 2005 SLRC/Mexicali inventory included three brick kiln ar eas: Colonia Mezquite in SLRC (the largest brick kiln area), Colonia La Grullita in SLRC (a smaller brick kiln area), and south of Algodones in no rtheast Mexicali (firing bricks and tile shingles). • Mexicali includes two brick kiln areas – Algodones and Colonia Campestre • Accurate activity data could not be collected at Colonia Campestre • The emissions from the Algodones brick kiln area were used. • The emissions from the Colonia La Grullita area in SLRC we re assumed to be equivalent to the Colonia Campestre area in Mexicali.
SAMPLE CALCULATIONS: Not applicable



### A-30SOURCE TYPE: Area SOURCE CATEGORY: Charbroiling/Street Vendors

DESCRIPTION: Emissions from charbroiling of meat. POLLUTANTS: NO x , VOC, CO, PM 10 , PM 2.5 METHOD: Estimates from Mexico NEI (ERG, 2006) SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Not applicable
EMISSION FACTORS:
• Not applicable NOTES AND ASSUMPTIONS:
• Data request for meat quantities, but adequate data were not available. • Mexicali emission estimates were used from the 1999 Mexico NEI. • Emissions were grown from 1999 to 2005 using population.
SAMPLE CALCULATIONS: Estimate the total annual emissions from charbroiling in Mexicali. 1999 annual CO emissions = 121.7 Mg/yr 1999 population 764,602 2005 population 855,962 Growth factor = 855,962/764,602 = 1.1195 2005 annual CO emissions = 121.7 Mg x 1.1195 = 136.2 Mg/yr = 150.2 tons/year



### A-31SOURCE TYPE: Area SOURCE CATEGORY: Construction Dust

DESCRIPTION: Dust emitted from earthmoving and other construction activities. POLLUTANTS: PM 10 and PM 2.5 METHOD: Estimates from 1999 Mexico NEI (ERG, 2006) SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Not applicable
EMISSION FACTORS:
• Not applicable NOTES AND ASSUMPTIONS:
• Mexicali emission estimates were used from the 1999 Mexico NEI. • Emissions were grown from 1999 to 2005 using population.
SAMPLE CALCULATIONS: Estimate the total annual emissions from construction dust in Mexicali. 1999 annual PM 10 emissions = 110.2 Mg/yr 1999 population 764,602 2005 population 855,962 Growth factor = 855,962/764,602 = 1.1195 2005 annual PM 10 emissions = 110.2 Mg x 1.1195 = 123.4 Mg/yr = 136.0 tons/year



### A-32SOURCE TYPE: Area SOURCE CATEGORY: Bakeries

DESCRIPTION: Emissions from bakeries. POLLUTANTS: VOC METHOD: Estimates from 1999 Mexico NEI (ERG, 2006) SPATIAL ALLOCATION: Population
ACTIVITY DATA:
• Not applicable
EMISSION FACTORS:
• Not applicable NOTES AND ASSUMPTIONS:
• Mexicali emission estimates were used from the 1999 Mexico NEI. • Emissions were grown from 1999 to 2005 using population.
SAMPLE CALCULATIONS: Estimate the total annual emissions from bakeries in Mexicali. 1999 annual PM 10 emissions = 95.6 Mg/yr 1999 population 764,602 2005 population 855,962 Growth factor = 855,962/764,602 = 1.1195 2005 annual PM 10 emissions = 95.6 Mg x 1.1195 = 107.0 Mg/yr = 117.9 tons/year



### A-33SOURCE TYPE: Area SOURCE CATEGORY: Landfills

DESCRIPTION: Landfill gases emission by anaerobic processes in landfills. POLLUTANTS: VOC and CH 4 METHOD: Theoretical first-order kinetic model of methane production SPATIAL ALLOCATION: Landfill location
ACTIVITY DATA:
• Average refuse acceptance rates (R) – 880 Mg/day or 321,200 Mg/yr (Villegas Ibarra, 2008)
EMISSION FACTORS : • Calculated values NOTES AND ASSUMPTIONS:
• Methane generation pot ential (Lo) – 100 m 3 CH 4 /Mg refuse. • Methane generation rate constant (k) – 0.02 yr -1

. • Time since landfill closure (c) – 0 years. • Time since initial refuse placement (t) – 15 years (Villegas Ibarra, 2008) • Molecular weight of CH 4 (MW CH4 )– 16.04 g/gmol • Molecular weight of NMOC (MW NMOC ) (expressed as hexane) – 86.18 g/gmol. • Temperature of landfill gas – 25 oC. • Concentration of NMOC in landfill gas (C NMOC )– 4000 ppmv. • VOC is assumed to be equivalent to NMOC.
SAMPLE CALCULATIONS: Estimate annual VOC and CH4 emissions from the Mexicali landfill. Q CH4 (methane generation, m 3 /yr) = L o R(e -kc – e -kt ) = 100 x 321,200 x (e [-0.02 x 0] – e [-0.02 x 15] ) = 8,324,919 m 3 CH 4 /yr
M CH4 = Q CH4 x {[MW( CH4 ) x 1 atm]/[(8.205 x 10 -5 m 3 -atm/gmol-K) x (1000 g/kg) x (273 + T)]} = 8,324,919 m 3 CH 4 /yr x {[16.04 g/gmol x 1 atm]/[(8.205 x 10 -5 m 3 -atm/gmol-K) x (1000 g/kg) x (298 K)]} = 5,461,218 kg/yr = 6,019.9 tons CH 4 /yr Q NMOC (NMOC generation, m 3 /yr) = 1.82 x Q CH4 x C NMOC /(1 x 10 6 ) = 1.82 x 8,324,919 m 3 CH 4 /yr x 4000/(1 x 10 6 ) = 60,605 m 3 /yr
M NMOC = Q NMOC x {[MW( NMOC ) x 1 atm]/[(8.205 x 10 -5 m 3 -atm/gmol-K) x (1000 g/kg) x (273 + T)]} = 60,605 m 3 NMOC/yr x {[86.18 g/gmol x 1 atm]/[(8.205 x 10 -5 m 3 -atm/gmol-K) x (1000 g/kg) x (298 K)]} = 213,611 kg/yr = 235.5 tons NMOC/yr



### A-34SOURCE TYPE: Area SOURCE CATEGORY: Wastewater Treatment

DESCRIPTION: Volatile organic compound emissions from collection, cont aminant treatment, and/or storage of industrial wastewater streams. These streams are discharged into either a receiving body of water or a municipal treatment plant. POLLUTANTS: VOC METHOD: Emission factors
ACTIVITY DATA:
• Municipality level municipal treatment plant statistics (CNA, 2007)
EMISSION FACTORS:
• TOG – 1.3 x 10 -5 kg/liter (U.S. EPA, 1991) NOTES AND ASSUMPTIONS:
• VOC emissions equal TOG emissions.

SAMPLE CALCULATIONS: Estimate the total annual VOC emissions fr om wastewater treatment in Mexicali. Actual flow rate = 1.8494 m3 /s = 58,322,678,400 liters/year Annual TOG emissions = 1.3 × 10 -5 kg/liter × 58,322,678,400 liters/year = 758,195 kg/year = 835.8 tons/year



### A-35

## APPENDIX B: TECHNICAL REPORT (D RAFT), ESTIMATION OF TRAVEL DEMAND AND VEHICULAR TRAFFIC CONGESTI ON OF THE MEXICALI REGION

## TECHNICAL REPORT (DRAFT)

Estimation of Travel Demand and Vehicular Traffic Congestion of the Mexicali Region

September 2008

Prepared by: 

# TransEngineering

for Eastern Research Group, Inc.



### B-1

# I. Introduction

The present document descri bes the approach followed for estimation of motor-vehicle activity and related mobile source emissions for the munici pality of Mexicali. As shown in Figu re 1, the municipality of Mexicali is located in the State of Baja California, Mexico, and includes the city of Mexicali.

City of Mexicali (urban area)

Limits of the Mexicali municipality

Figure 1. Geographic location of the Mexicali municipality.
The municipality of Mexicali has an area of 5,650 square-miles, while the urban area of Mexicali has only 54 square-miles, thus the urban area represents about 1% of the entire municipality area. Nevertheless, based on INEGI statistics, the 2005 population for the urban area of Mexicali was 653,000, which represents approximately 88% of the total municipality population 1

.

1 According to INEGI, the populatio n of the municipality of Mexicali was 745,000 in 2005 (Instituto Nacional de Estadística, Geografía e Informática/ Conteo de Población 2005). 

### B-2The main roadways in the Mexicali municipality have been identified, for both urb an and rural areas; these roadways are depicted in Figure 2. In the rural areas , the most important roadways are Federal Highway 2 (MEX 2), which connects the city of Mexicali to the city of Tijuana (to the west), and San Luis Rio Colorado (to the east), and Federal Highway 5 (MEX 5) which connects the city of Mexicali to San Felipe (to the south) and Ensenada (to the southwest). The rest of the roadways identified in Figure 2 are state highways. As part of the roadway infrastructure, three ports-of-entry (POEs) between Mexico and the US have been identified as well: the Andrade POE in northeast corner of the municipality, and the Calexico and Calexico East POEs in the city of Mexicali. Figure 3 presents a map wi th details of the urban area of Mexicali.

A ndrade POE Calexico POE Calexico East POE

BC 15 B C 6 B C

2

B C 1

B C

10 BC 4 M

EX 2 M E X 2

M E X 5

M

E X 5

Figure 2. Main rural and urban roadways within the Mexicali municipality.



### B-3Calexico POE Calexico East POE

Figure 3. Detail of roadway system for t he city of Mexicali (urban area).
The rural and urban roadway systems have been converte d into a travel demand model to estimate daily vehicle traffic volumes. The travel demand model de veloped for this study has three main components:

• Trip generation • Trip distribution • Traffic assignment Each of these components will be briefly described in the following pages.



### B-4

## II. Trip Generation

As a first step to establish traffic volumes in Mexica li, the methodology takes advant age of research in the US indicating that the values of trip generation rates acro ss urban area locations and sizes are fairly stable when disaggregated by socioeconomic conditions of t he population such as household size, income, and employment. This stability intuitively suggests the transfe rability of known rates from the limited case studies in Mexico to other urban areas in the country. One known and documented case study is Ciudad Juarez, and thus its disaggregate trip rates as well as other traffic patterns can be borrowed and used as a basis for estimating preliminary traffic volumes in other urban areas. For the present study , such rates will also be assumed to take place in the ru ral areas of the municipality.
Trip generation rates The trip generation rates developed from the Juarez’ transportation research program 2 are shown in Tables 1 and 2. Table 1 presents production rates based on house hold size and household income. Table 2 presents attraction rates based on area type and employment by economic activity. These rates are expressed as person-trips per day. Table 1. Juarez’ disaggregate trip production rates (daily person-trips per household). Trip HH HH size purpose income 1 2 3 4 5 6+ (1) 0.508 1.156 1.268 1.417 1.474 2.067 (2) 0.902 1.479 1.705 1.726 2.096 2.400 HBW (3) 0.875 1.522 1.581 1.698 2.247 2.987 (4) 1.000 1.708 1.847 1.800 2.438 2.762 (5) 0.598 1.541 1.559 2.169 2.494 3.259 (6) 0.801 2.307 2.180 2.119 2.622 3.196 (1) 2.609 3.761 6.142 9.435 13.264 13.111 (2) 1.647 3.792 6.005 9.294 12.064 13.730 HBO (3) 1.933 3.921 5.767 9.221 12.955 13.430 (4) 2.580 3.143 4.536 11.005 12.653 17.027 (5) 1.933 4.349 7.240 10.707 10.839 12.742 (6) 0.769 2.675 4.957 10.475 13.960 11.975 (1) 0.435 0.560 1.484 1.252 2.604 2.396 (2) 0.352 0.820 1.515 1.891 2.479 2.335 NHB (3) 1.019 0.976 2.301 2.583 2.577 2.314 (4) 1.230 1.659 1.766 3.550 3.299 3.060 (5) 1.230 2.167 3.890 3.198 3.587 3.060 (6) 0.609 1.882 1.726 2.929 5.643 1.995 The income ranges are as follows: Range Household income range Household income range Code (1996 daily min wages) (1996 dollars)

1 0 to 2 $ 0.00 to $ 1,374 /year 2 2 to 6 $ 1,375 to $ 4,122 /year 3 6 to 10 $ 4,123 to $ 6,870 /year 4 10 to 12 $ 6,871 to $ 8,244 /year 5 12 to 18 $ 8,244 to $12,367 /year 6 over 18 $12,368 plus

2 Technical Memo series “Estudio Integral de Transporte/Multimodal Transportation Study: Development of Travel Demand and MobileSource Emission Models for base year 1996, Juarez”, (IMIP 1998). 

### B-5Table 2. Juarez’ disaggregate trip attraction rates (daily person-trips per employee). Trip Area Household Employment type purpose Type Basic Retail Service CBD 0.125 2.000 1.557 1.899 HBW Urban 0.125 1.859 3.351 2.953 Suburban 0.125 1.932 1.777 2.027 Rural 0.125 1.497 1.615 2.099 CBD 0.569 0.031 22.417 36.758 HBO Urban 0.569 0.084 16.275 28.701 Suburban 0.569 0.036 20.086 29.457 Rural 0.569 0.195 3.423 2.881 CBD 0.127 0.062 14.438 0.409 NHB Urban 0.127 0.505 7.179 3.568 Suburban 0.127 0.109 2.681 4.875 Rural 0.127 0.627 10.485 2.694 CBD 0.160 0.002 0.000 0.000 TT Urban 0.170 0.000 0.129 0.000 Suburban 0.180 0.012 0.549 0.102 Rural 0.180 0.087 0.477 0.028 As depicted in these tables, the trip rates have been disaggregated into four different trip purposes: 1) Home-based-work (HBW) trips, which have the home as one of the trip ends, and work on the other, regardless of which is the origin or destination. 2) Home-based-other (HBO) trips, which have the home as one of the tr ip ends, and a non-work activity on the other, regardless of which is the origin or destination. 3) Non-home-based (NHB) trips, which have non-home trip ends. 4) Truck-taxi (TT) trips, which are delivery type NHB trips. Table 2 shows in addition four different categories of Area Type. This is a measure of urban activity concentration also known as activity density. Such a ttribute needs to be computed for the different component zones of each urban area. Activity density is a func tion of the population and employment per unit area, as follows: Activity Density = [Zone Population + (Zone Employment * F)] / Zone Acres ( Eq. 1 ) ...where the normalization factor F = Total study area Population / Total study area Employment ( Eq. 2 ) Area Type thus is categorized as follows: Code

4 3 2 1 Area Type Rural Suburban Urban CBD Activity density up to 26 27 to 62 63 to 135 over 136

Zone structure In order to develop trip generation patterns from expansion of the disaggregate trip rates, a zone structure with the required demographic variables like population, hous ehold size, income, and employment was established for the municipality of Mexicali. In this regard the Instituto Nacional de Estadística Geografía e Informática (INEGI) has demographic and socio-economic informat ion, disaggregated in census tracts called Areas Geoestadísticas Básicas (AGEBs). These AGEB arrang ements were used as reference to develop a traffic analysis zone (TAZ) structure for the urban and rural area s of the municipality. Fi gure 4 shows the resulting TAZ structure, which follows the alignment of the main roadway system . This TAZ structure was developed into a geographic information system (GIS) coverage, to store zonal attributes of the municipality. 

### B-6Detail of TAZ structure for urban Mexicali

Figure 4. Traffic Analysis Zone (TAZ) structure for the Mexicali municipality.
Disaggregate household information To develop total productions by TAZ, the trip generation model requires as input the total number of households under each category of size and income for each TAZ. Regarding income, INEGI reports such variable on a per capi ta basis, so under the Ju arez case study a linear regression was developed to correlate per capita in come to a zonal household average income value, specially requested to INEGI from the 1990 cens us. The regression has the following form: RelHHinc = 3.230 – 0.125*W a – 0.078*W b – 0.059*W c ( Eq. 3 ) where: RelHHinc: Zonal average household income/Regional average household income W a : Zonal Pop earning less than 1 minimum wage x100/Total zone pop W b : Zonal Pop earning between 1 and 2 minimum wages x100 /Total zone pop W c : Zonal Pop earning between 2 and 5 minimum wages x100/Total zonal pop Household size is reported by INEGI as a zonal average at the AGEB level; this number was converted to a relative household size (RelHHsiz), dividing the zonal average household size value by the regional average household size value. 

### B-7In order to convert the previously described zonal aver ages to actual number of households on each income and size category, the marginal curves in Figures 5 and 6 were developed. These are used by entering the relative zonal average (zonal average/regional average) on the X axis, and obtaining the percentage of households under each category, depicted by the corres ponding marginal curves. The percentages are then applied to the total number of households in the TAZ.

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 HH inc 1 Adj 1 HH inc 2 Adj 2 HH inc 3 Adj 3 HH inc 4 Adj 4 HH inc 5 Adj 5 HH inc 6 Adj 6

Relative HH income Figure 5. Distribution of households by relative household income (RelHHinc).

1.2

1.0

0.8

0.6

0.4

0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 HH size 1 Adj 1 HH size 2 Adj 2 HH size 3 Adj 3 HH size 4 Adj 4 HH size 5 Adj 5 HH size 6 Adj 6

Relative HH size Figure 6. Distribution of households by relative household size (RelHHsiz).%HHs%HHs

The marginal curves were also developed under t he Juarez’ transportation research program, using extensively detailed databases specially requested to INEGI from the 1990 census. 

### B-8Disaggregate employment information Employment by economic activity for each TAZ is requir ed by the trip generation model to establish trip attraction patterns. This information was obtained dire ctly from the “Censos económicos 2004” (INEGI 2004) for the Mexicali area. Since this information is availa ble only for urban areas of 50,000+ population, there is no employment information for rural areas in Mexico, and thus employment distributi on for the rural area of the municipality was assumed to follow similar patterns as those observed in the urban setting; therefore an employee was allocated for every 4 people living on every rural TAZ.
Balanced trip generation totals by AGEB As previously indicated, the production trip rates ar e disaggregated by household size and income, therefore the total productions for a zone were obtained simply by multiplying the production trip rates (Table 1) times the number of households under each category of household size and income. Similarly, total attractions for each zone were obtained by multiplying the attraction trip rates (Table 2) times the number of employees under the corresponding category of area type and economic activity. The end result of this process is a production-attraction table by zone, for each trip purpose. In theory these productions and attractions under the same trip purpose need to add up to the same number, but since their development are based on different sources (household survey for produ ctions, and workplace survey for attractions), it is common practice to “balance” or factor one to the other, usually to the more reliable one. For the Juarez’ case, and thus for the study domain, attractions were balanced to productions. In the case of the TT trip purpose, the attractions were balanced to a control total equal of POP/20.9, which was the validated relationship in Juarez. Table 3 shows a summary of raw production and attraction totals by trip purpose as well as the resulting balance factors.

Table 3. Resulting P-A (person-trips/day) totals for the Mexicali municipality.

unbalanced F A balanced
HBW P 390,000 390,000 A 393,676 0.9907 390,000 HBO P 364,950 364,950 A 311,433 1.1718 364,950 NHB P 1,820,513 1,820,513 A 1,352,227 1.3463 1,820,513 TT P 35,477 35,477 A 55,374 0.6407 35,477 Total Ps 2,610,940 Total As 2,610,940

The balanced P-A by TAZ was stored in the zonal (TAZ) GIS.



### B-9

## III. Vehicle-Trip Distribution

At the trip generation step, the total number of trips produced from, and attracted to each TAZ were estimated and presented in tabular format. This step provides a rough estimate of the trip-making potential for each zone, but does not determine the actual trip exchange with other zones. The trip distribution step focuses on this trip exchan ge between zones, identifying from where and to where trips take place. Trip exchange between zones is conv entionally presented as a two-dimensional matrix array, where each cell represents the number of trips produced at zone i (row i ) and attracted to zone j (column j ). Next, this Production-Attraction matr ix (also referred to as the “P-A matrix”) is transformed to OriginDestination (O-D), simply by reconfiguring the cell va lues to produce a matrix symmetric around the main diagonal. In this regard it is important to remember that only in the case of NHB trips, Production is considered synonymous to Origin and Attraction to Destination.
The traditional gravity model The traditional gravity model was originally developed fr om analogies with the physical world (Newton’s gravity formulations), but its real strength comes from later improvements and derivation through entropy-maximizing considerations, which makes the model closely related to information theory, to error measures, and to maximum likelihood in statistics. The doubly constrained version of the traditional gravity model has the following form:

T ij = β i * P i * α j * A j * f(t ij ) ( Eq. 4 )

Where: T ij : Trips produced in zone i and attracted to zone j
. P i : Total trips produced in zone i
. β i : Balancing factor for row i (production constraint). A

j : Total trips attracted to zone j
. α

j : Balancing factor for column j (attraction constraint) f(t ij ): Impedance (decreasing) function, based on the travel time between zone i and zone j
.

The two constraints that the model is required to meet ar e 1) that the sum of trips in any specific row of the matrix should equal the total number of trips produced in t hat zone, and 2) that the sum of trips in any specific column should correspond to the number of trips attrac ted to that zone (refer back to Figure 5). The two conditions can be written as:


# Σ T ij = P i ( Eq. 5 ) j 

# Σ T ij = A j ( Eq. 6 ) i



### B-10The expression of both balancing factors β i and α j can thus be derived through simple algebraic manipulations of Equations 4 to 6. These have the following forms:
1 β i = ( Eq. 7 ) α

j * A j * f ( t i j ) 

# Σ j 1 α

j = ( Eq. 8 ) β i * P i * f ( t i j ) 

# Σ

i
As shown here, the balancing factors are interdependent, meaning that the calculation of one set requires the values of the other set, furthermore suggesting an iter ative process until convergence is achieved. Thus, the practical approach to solving this formulation is to specify separate singly constrained models to both productions (Eq. 9) and to attractions (Eq. 10). The first one is obtained by making α j = 1 since in this case the columns are not being balanced. Similarly, the second one is obtained by making β i = 1 since in this other case the rows are the ones not being balanced.

A j * f(t ij ) T ij = P i * ( Eq. 9 ) A

j * f(t ij ) 

# Σ j P i * f(t ij ) T ij = A j * ( Eq. 10 ) P i * f(t ij ) 

# Σ

i The solution for the doubly constrained model can then be converged upon by iteratively applying Eq. 9 to balance the productions (rows), and Eq. 10 to balance attractions (columns). Transportation networks In order to perform trip distribution, a roadway network needs to be developed for each urban area selected. This network is a simplified version of the current r oadway infrastructure layout, including only the main roadways, federal and state highways in the rural setting, and from principal arterials to collector roads in the urban setting. Local roadways and streets are modeled by artificial links called connectors, which allow for the channeling of flows between the TAZs (TAZ centroids) and the network system. The networks were drawn from available maps, and organized in a roadway GIS. Figure 7 shows such primary roadway network developed for the Mexicali municipality. Each link in the network was initially provided with attri butes of functional class (generic type of infrastructure facility), and direction of flow. This information on th e urban setting was provided by Mexicali’s IMIP (Instituto Municipal de Investigación y Planeación), while for the rural setting this information was established through visual interpretation of satellite images. From this in formation, a second set of network link attributes was established, based on average conditions developed for the Ciudad Juarez case study. This information was link capacity and average daily speed, corresponding to functional class and area type where individual links are located, as depicted in Table 4. Link travel time was then computed using the assigned link speed.



### B-11Detail of roadway network for urban Mexicali

Figure 7. Roadway network model for the Mexican domain.

Table 4. Speed & capacity (daily per lane) lookup table developed for Juarez.
Speed (mph) Area Type Capacity (vpl) CBD (1) Urban

(2) Suburban (3) Rural

(4) Conn 15 15 25 35

(0) 30,000 30,000 30,000 30,000 Fwy 29 29 31 38tion (2) 17,250 17,250 14,750 11,750 Expy 25 25 29 36ifica (3) 13,100 13,100 11,750 10,250 PartD 17 17 26 33Class (4) 8,350 8,350 7,500 6,250 PartU 17 17 25 32ctional (5) 7,550 7,550 6,800 5,600 MartD 14 14 24 28 (6) 7,250 7,250 6,500 4,050 MartU 14 14 24 28Fun (7) 6,600 6,600 5,950 3,750 MartUnp 11 11 18 25 (8) 6,200 6,200 5,550 3,350 Ramp 17 17 24 34 (12) 18,000 18,000 18,000 18,000 

### B-12Roadway networks are needed at this stage of the modelin g process mainly to establish travel times between TAZs, since this will feed into the gravity-model evaluation. Using the network travel times, a skim matrix was developed for the domain. This skim matrix depicts the sh ortest travel time (in minutes) between all TAZs in the study area.
Evaluation of gravity model The end result of the gravity-model estimation-calibration process, is the definition of the impedance function (the f(t ij ) term). For the Juarez case study, doubly-constrained gravity models were estimated and calibrated for the four trip purposes, yielding impedance functions in the form of friction factor (FF) tables. Under the current project, these FF tables were borrowed to evaluate gravity models for each of the selected urban areas, using the corresponding set of skim matrices and P-A tables. The result of this evaluation, was the development of trip distribution P-A matrices by trip purpose, for the Mexicali municipality. The FF tables for each trip purpose are shown in Appendix A.
Development of vehicle-trip OD matrices As a final task of the trip distribution process, the all-mode person-trip P-A matrices needed to be converted into vehicle-trip O-D matrices. First, this requires the application of mode shares (spec ifically AUTO mode), to the total number of persontrips by trip purpose. Secondly, person-trips on AUTO need to be converted into vehicle-trips, by applying a passenger occupancy rate to the person-trip figure. From the Juarez research, these AUTO shares and occupancy rates were observed as follows: person-trip average AUTO share veh occupancy HBW purpose 57.7% 1.09 pax/veh HBO purpose 44.4% 1.39 pax/veh NHB purpose 66.1% 1.22 pax/veh TT purpose 100.0% 1.00 pax/veh In the Juarez Case study, vehicle-m iles traveled (VMT) from TRANSIT mode represents in the order of 2% of the total daily VMT, thus such vehicle activity has been emulated in the VMT mix at the emission modeling stages. The same approach has been follow ed for the initial six urban area categories. Finally, in addition to the four generic trip purposes c haracterizing local (internal to the area) trips, an additional trip purpose was added at this stage, depicting external trips (EXT). This trip purpose actually includes all external-external and external-local trips, that is, trips that have at least one of their trip-ends outside of the study area. In order to develop the corresponding O-D matrix for EXT trip purpose, counts were obtained as control totals for the two POEs 3 and the most important highways connect ing the Mexican municipality to other points outside the study area. Table 5 presents a summa ry of daily vehicle-trips estimated for the Mexican domain. Table 5. Summary of daily vehicle-trips estimated for the Mexicali municipality. daily vehicle-trips HBW 193,189 HBO 582,286 NHB 211,303 TT 35,477 EXT 104,200 TOTAL 1,126,455
3 In year 2005, the Calexico, Calexi co East and Andrade POEs had daily averages of 17000, 9900 and 2000 northbound vehicle crossings, respectively. 

### B-13

## IV. Traffic Assignment

The final step in the travel demand modeling process and traffic congestion estimation is traffic assignment. In this step, the final OD vehicle-trip matrix for each urban area was assigned to the corresponding network system, using a user-equilibrium (UE) algorithm. Such algorithm assigns volumes to the network links, and through iteration balances congestion levels between similar cost (time) alternatives. The process involves recomputation of travel speeds to account for congestion le vels as traffic volumes accumulate. The result of this step yielded “loaded” networks for the selected urban area s with traffic volumes and speeds at each of the links of the network. A separate assignment was obtained for weekday and weekend conditions. The weekend assignment was established by applying a factor of 0.7 to the weekda y OD matrix. Figure 8 shows a thematic map of the loaded network for the Mexicali municipality under weekday conditions.

Detail of roadway loaded network for urban Mexicali

Figure 8. Loaded roadway network under weekday conditions for the Mexican domain. This information was used to estimate on-road mobile source emissions by link, an then aggregated at the municipality level. 

### B-14

### V. On-road mobile source emissions
Having completed the travel demand modeling part of the study, the final task require s converting the resulting daily vehicle activity (motorized travel) into total daily on-road mobile source emissions, for the entire Mexicali municipality. . Base emission rates Emission rates in grams/mile-of-travel (or grams/VMT) for VOC, CO, NOx, SO 2 , NH 3 , PM 2.5 , and PM 10 , have been obtained through the use of Mobile6-Mexico, for a co mbination of vehicle speeds going from 3 to 65mph. In addition, emission rates have been obtained for a range of temperatures depicting average ambient conditions at four different time-of-day periods, during four generic seasons of the year. The time-of-day periods considered were: period 1 7am to 9am period 2 9am to 4pm period 3 4pm to 8pm period 4 8pm to 7am The four generic seasons were designed to represent spring, summer, fall, and winter.
Emissions by link Having developed the base emission rates for the result ing combinations of speeds and temperatures, these were assigned to each link in the transportation netwo rks, based on the prevailing “congested” speed at a specific point in time during the day. For this, th e daily non-directional traffic and 24-hour average speeds assigned to each link in the travel models needed to be c onverted to time-of-day directional traffic and time-ofday directional speeds. To do this a special computer program labeled PrepinPlus was coded in Visual Basic (VB) language, following the methodology developed by the Texas Transportation Institute on its software PREPIN. The following sequence summarizes the proces s, which needs to be done for each of the four timeof-day periods: a) The 24-hour non-directional traffic assigned to each network link is distributed over the four time-of-day periods, based on information from ground counts. For Mexican roadway networks the daily traffic volumes have been di stributed as follows, based on the Juarez case study: period 1 9.8% over a 2-hour period period 2 39.8% over a 7-hour period period 3 26.0% over a 4-hour period period 4 24.4% over an 11-hour period 100.0% b) To establish the directional split, the period non-directional traffic volume on each link is then applied a factor, which depends on the sp ecific time-of-day period being analyzed. These factors vary also depending on the ar ea type and functional classification of the roadway. Table 6 shows the average directional factors used for the AB link direction; the complement to 1 is applied to the BA direction of each link. At this point then every link in the network has directional traffic volumes for the time-of-day period being evaluated.



### B-15Table 6. Directional split factors used for Juarez. 

### period 1 Functional classification 0 1 2 3 4 5 6 7 8 9 10 11 12Area Type 1 0.540 0.650 0.500 0.650 0.650 0.650 0.580 0.580 0.645 0.645 0.500 0.500 0.500 2 0.540 0.650 0.500 0.650 0.650 0.650 0.580 0.580 0.645 0.645 0.500 0.500 0.500 3 0.870 0.600 0.500 0.600 0.600 0.600 0.590 0.590 0.630 0.630 0.500 0.500 0.500 4 0.760 0.620 0.630 0.665 0.665 0.665 0.650 0.650 0.738 0.738 0.630 0.630 0.630 

### periods 2 and 4 Functional classification 0 1 2 3 4 5 6 7 8 9 10 11 12Area Type 1 0.540 0.550 0.510 0.550 0.550 0.550 0.550 0.550 0.545 0.545 0.510 0.510 0.510 2 0.540 0.550 0.510 0.550 0.550 0.550 0.550 0.550 0.545 0.545 0.510 0.510 0.510 3 0.540 0.540 0.510 0.540 0.540 0.540 0.550 0.550 0.530 0.530 0.510 0.510 0.510 4 0.540 0.565 0.510 0.565 0.565 0.565 0.565 0.565 0.555 0.555 0.510 0.510 0.510 

### period 3 Functional classification 0 1 2 3 4 5 6 7 8 9 10 11 12Area Type 1 0.550 0.620 0.540 0.620 0.620 0.620 0.520 0.520 0.575 0.575 0.540 0.540 0.540 2 0.550 0.620 0.540 0.620 0.620 0.620 0.520 0.520 0.575 0.575 0.540 0.540 0.540 3 0.720 0.590 0.540 0.590 0.590 0.590 0.530 0.530 0.645 0.645 0.540 0.540 0.540 4 0.715 0.615 0.665 0.615 0.615 0.615 0.645 0.645 0.648 0.648 0.665 0.665 0.665
c) Next, the average directional 24-hour link capacities provided as attributes of the roadway network are converted into dire ctional hourly capacities. This is done through another set of conversion factors that also depend on t he area type and functional classification of the link. Table 7 shows the factors used; these remain constant during the day. Table 7. Conversion factors for 24-hour to hourly capacities.
Functional classification 0 1 2 3 4 5 6 7 8 9 10 11 12Area Type 1 N/A 0.063 0.104 0.063 0.066 0.066 0.076 0.076 0.073 0.070 0.070 0.066 0.061 2 N/A 0.063 0.104 0.063 0.066 0.066 0.076 0.076 0.073 0.070 0.070 0.066 0.061 3 N/A 0.077 0.081 0.077 0.080 0.081 0.092 0.092 0.086 0.083 0.083 0.080 0.067 4 N/ A 0.106 0.166 0.106 0.116 0.121 0.173 0.167 0.164 0.159 0.159 0.112 0.078 By multiplying the directional hourly capacitie s by the hours in each period, these are then turned into directional link capacities for the entire time-of-day period. Having established both directional traffic volume and capacity for each link, for a specific time-of-day period, the link’s v/c ratio on each direction can be computed.
d) To establish the directional time-of- day speed, the DFW algorithm in the PREPIN methodology was used. For this algorithm the free-flow speed for each link needs to be established, and for that purpose Table 8 pr ovide average values depending on the link’s specific area type and functional classification. For each link, this free-flow speed goes into the DFW algorithm together with the av erage 24-hour speed, and the directional v/c ratio, thus yielding the directional congested speed. Table 8. Free-flow speeds in mph.
Functional classification 0 1 2 3 4 5 6 7 8 9 10 11 12Area Type 1 15.00 50.00 55.00 50.00 11.90 11.90 11.90 11.90 11.90 11.90 11.90 11.90 16.90 2 15.00 50.00 55.00 50.00 11.90 11.90 11.90 11.90 11.90 11.90 11.90 11.90 16.90 3 25.00 50.00 55.00 50.00 25.40 25.40 20.00 20.00 19.40 19.40 25.40 25.40 30.40 4 35.00 51.00 57.00 51.00 36.40 36.40 31.30 31.30 30.00 30.00 30.00 36.40 41.40



### B-16Converting link congestion into emissions by-link The directional speed on each link (established for specific time-of-day periods) is then used to establish the link’s emissions rate to use for VOC, CO, NOx, SO 2 , NH 3 , PM 2.5 , and PM 10 . These Mobile6-Mexico rates are provided as a lookup table on the VB code and used to assign the resulting rates to each link. Having established the emission rates for each link, t hese are then multiplied by the link’s length and traffic volume (i.e. link VMT) in the period to obtain the link’s emissions concentration per time-of-day.

Annual mobile source emissions estimation The PrepinPlus code was run using the traffic loaded netwo rks, under each of the seasonal temperatures, aswell as for weekday and weekend conditions. The resulting emissions-by-link were added for the four time-ofday periods to establish daily emissions-by-link. Thes e total daily emissions-by-link were aggregated for the entire Mexicali municipality. Finally the weekday and weekend emissions were aggregated by season, considering 13 weeks per season, and within each week, 5 weekdays and 2 weekend days. The seasonal emissions for spring, summer, fall and winter were added to establish annual emissions. The annual on-road mobile source emissions estimated for the Mexicali municipality are summarized by pollutant on Table 9. Table 9. Annual on-road mobile source emissions (8 vehi cle classes) for the Mexican municipality (2005)

VOC* CO NOx SO 2 NH 3 CH 4 PM 2.5 PM 10 LDGV [Tm/year] LDGT12 [Tm/year] LDGT34 [Tm/year] HDGV [Tm/year] LDDV [Tm/year] LDDT [Tm/year] HDDV [Tm/year] MC [Tm/year] A LL [Tm/year] 4,128 2,163 1,098 357 9 5 309 75 8,143 26,709 15,943 8,234 2,246 17 10 1,428 392 54,979

1,045 1,019 363 340 11 7 4,956 34 7,775 23 21 9 4 0 0 96 0 154 113 79 24 3 0 0 10 0 229

53 39 17 6 0 0 12 4 132 107 104 37 6 2 1 347 0 604 130 123 44 8 2 1 385 1 694 * Emissions for VOC include exhaust and evaporative



### B-17

### VI. Paved and Unpaved Road Dust
In addition to the particulate matter (PM) emissions estimated from vehicle exhaust and tire/break wear (shown in Table 9), in a separate exercise PM was al so estimated from roadway dust airborne by vehicle movement. The amount of airborne dust is a function of the amount of dust on the roadway, and also of the vehicle traffic volume (VMT: vehicle-miles of trav el) and average vehicle speed of a roadway, which are conditions estimated from the travel demand model. In most cases, the functional class of each link will indicate if it is paved or unpaved (FUNCL=8 indicates unpaved condition), and therefore which PM emission fa ctor is appropriate. Yet, in the special case of centroid connector links (FUNCL=0), this requires an additi onal task, since centroid connectors are virtual links representing multiple local roadways within a TAZ. If a proportion of such local roadways in a given TAZ are unpaved, this needs to be represented in the selected emis sion factors for the TAZ’s centroid connector link. Figure 9 presents a map of the TAZs with categories of roadway pavement coverage 4

.

Pavement coverage detail for urban Mexicali

Figure 9. Roadway pavement coverage of TA Zs in the Mexicali municipality.

4 Coverage by zone provided by the Mexicali IMIP. 

### B-18Paved and unpaved VMT For proper characterization of PM from paved and unpaved roadways, the VMT obtained with the travel demand model was also identified by the paved/unpaved categories. On the one hand, the VMT on all centroid connector links (red links in Figure 9) was divided between paved and unpaved conditions proportionally to the roadway pavement coverage of the corresponding TAZ. In addition, some of the on-system links (gray links in Fi gure 9) were identified as unpaved as well (refer to Figure 2), and therefore the VMT over on-system links was also categoriz ed as paved or unpaved. Table 10 provides a summary of VMT over paved and unpav ed roadways in the Mexicali municipality.

Table 10. Summary of modeled VMT on paved and unpaved roadways for the Mexciali municipality (2005)

area [sq-miles] daily VMT %VMT paved unpaved Urban Mexicali 54 3,965,327 98.0% 2.0% Rural Mexicali 5,563 3,911,214 89.7% 10.3% Mexicali Municipality 5,617 7,876,541 93.9% 6.1%

Even though the apparent surface of TAZs with no pav ed roadways seems to domi nate over the study area, the actual miles of roadways in these TAZs is minimal. In terms of vehicle activity the effect is equivalent, and thus over 90% of the VMT in the Mexicali m unicipality takes place over paved roadways.
Paved and unpaved PM emissions from roadway dust Based on the dust sampled from roadways in the Mexi cali municipality, the following emission factors were estimated with Mobile6-Mexico: Paved roads, at any vehicle speed: - PM 10 of 0.00377 lbs/VMT 5 - PM 2.5 of 0.00034 lbs/VMT Unpaved roads, at 11mph (average speed on urban settings): - PM 10 of 0.35442 lbs/VMT - PM 2.5 of 0.03515 lbs/VMT Unpaved roads, at 18mph (average speed on suburban settings): - PM 10 of 0.45350 lbs/VMT - PM 2.5 of 0.04505 lbs/VMT Unpaved roads, at 25mph (average speed on rural settings): - PM 10 of 0.53453 lbs/VMT - PM 2.5 of 0.05316 lbs/VMT

Once every roadway link in the municipality was assigned and emission factor, and multiplied by its corresponding VMT, total PM for weekday and weekend day was estimated, as summarized in Tables 11 and 12.

5 Link VMT is estimated by multipl ying the daily traffic volume of each link by the link’s length (in miles). 

### B-19Table 11. Weekday PM emissions from roadway dust in the Mexicali municipality (2005)



### VMT PM 10 

### [metric Tons] PM 2.5 

### [metric Tons] paved roadways 7,395,242 12.5 1.1 unpaved roadways 481,299 115.0 11.4 all roadways 7,876,541 127.5 12.6

Table 12. Weekend day PM emissions from roadway dust in the Mexicali municipality (2005)

VMT PM 10 [metric Tons] PM 2.5 [metric Tons] paved roadways 5,158,478 8.7 0.8 unpaved roadways 337,933 80.8 8.0 all roadways 5,496,411 89.5 8.8

Using the daily emissions from Tables 11 and 12, yearly PM emissions from roadway dust is summarized on Table 13. Table 13. Yearly PM emissions from roadway dust in the Mexicali municipality (2005)



### PM 2.5 

### PM 10 

### paved roadways [metric Tons/yr] unpaved roadways [metric Tons/yr] A LL roadways [metric Tons/yr] 383 3,819 4,202 4,182 38,403 42,586

It can be seen that the PM emission factors for unpaved roadways are considerably higher than paved roadways, thus explaining why the amount of PM from unpaved roadways overpasses by far the PM from paved roadways, even though the VMT on paved roadways is much larger. Compared to PM from vehicle exhaust (Table 9), PM from roadway dust is considerably higher.



### B-20